Update to uniform US spelling

README.md

@@ -33,7 +33,7 @@ The Eval Cards project aims to:
 ## Architecture
 
 This app is a **read-only consumer** of a snapshot. The producer (`eval_card_backend`)
-canonicalises raw evaluation data into a typed Parquet warehouse plus a Stage J view
+canonicalizes raw evaluation data into a typed Parquet warehouse plus a Stage J view
 layer (`*.parquet` view tables + JSON sidecars), and the frontend reads that snapshot at
 runtime via DuckDB — it performs no identity resolution or aggregation of its own. The
 view-layer column names match this app's TypeScript interfaces by contract (declared in

app/developers/[...id]/layout.tsx

@@ -32,7 +32,7 @@ export async function generateMetadata(props: {
   const title = developerName
   const description =
     modelCount != null
-      ? `${developerName} — evaluation coverage across ${modelCount.toLocaleString("en-US")} ${modelCount === 1 ? "model" : "models"}, organised under Evaluation Cards' five-level hierarchy and four interpretive signals.`
+      ? `${developerName} — evaluation coverage across ${modelCount.toLocaleString("en-US")} ${modelCount === 1 ? "model" : "models"}, organized under Evaluation Cards' five-level hierarchy and four interpretive signals.`
       : `${developerName} — model evaluation coverage on Evaluation Cards.`
   const imageUrl = `/api/og/developers/${idSlug}`
 

app/layout.tsx

@@ -13,7 +13,7 @@ const SITE_URL = "https://evalcards.evalevalai.com"
 const SITE_NAME = "Evaluation Cards"
 const SITE_TITLE = "Evaluation Cards — a reporting layer for AI evaluations"
 const SITE_DESCRIPTION =
-  "A public collection of reported model–benchmark results, organised under a five-level rollout hierarchy and four interpretive signals: reproducibility, completeness, provenance, and comparability."
+  "A public collection of reported model–benchmark results, organized under a five-level rollout hierarchy and four interpretive signals: reproducibility, completeness, provenance, and comparability."
 
 export const metadata: Metadata = {
   metadataBase: new URL(SITE_URL),

app/models/[...id]/layout.tsx

@@ -31,7 +31,7 @@ export async function generateMetadata(props: {
   const idSlug = routeIdToPath(routeId)
   const title = developer ? `${modelName} — ${developer}` : modelName
   const description = developer
-    ? `${modelName} (${developer}) — every reported model–benchmark result, organised under Evaluation Cards' five-level hierarchy and four interpretive signals.`
+    ? `${modelName} (${developer}) — every reported model–benchmark result, organized under Evaluation Cards' five-level hierarchy and four interpretive signals.`
     : `${modelName} — every reported model–benchmark result on Evaluation Cards.`
   const imageUrl = `/api/og/models/${idSlug}`
 

app/opengraph-image.tsx

@@ -108,7 +108,7 @@ export default function OpenGraphImage() {
               maxWidth: "920px",
             }}
           >
-            A collection of reported model–benchmark results, organised under a five-level
+            A collection of reported model–benchmark results, organized under a five-level
             rollout hierarchy and four interpretive signals.
           </div>
         </div>

app/page.tsx

@@ -119,7 +119,7 @@ export default async function HomePage() {
             </h1>
             <p className="home-hero-lede">
               <strong>Evaluation Cards</strong> is a collection of reported model–benchmark results,
-              organised under a five-level rollout hierarchy and four interpretive signals
+              organized under a five-level rollout hierarchy and four interpretive signals
               computed over the joined record.
             </p>
             <div className="flex flex-wrap items-center gap-3">

components/quick-start.tsx

@@ -45,7 +45,7 @@ const SLIDES: Slide[] = [
     icon: BookOpen,
     kicker: "Welcome",
     title: "What this is",
-    body: "Evaluation Cards collects how AI models have been tested across lots of benchmarks. A single score rarely tells you much on its own, so we give you everything you need to read one: the evaluation results, the benchmark's metadata, how the run was set up, and our interpretive signals. And unlike any other artefact out there, you can see those results reported side by side, which makes them easy to compare.",
+    body: "Evaluation Cards collects how AI models have been tested across lots of benchmarks. A single score rarely tells you much on its own, so we give you everything you need to read one: the evaluation results, the benchmark's metadata, how the run was set up, and our interpretive signals. And unlike any other artifact out there, you can see those results reported side by side, which makes them easy to compare.",
   },
   {
     icon: Layers,

components/signals/comparability-panel.tsx

@@ -398,7 +398,7 @@ function DivergenceGroupItem({
   const fieldLabels = fields.map((f) => formatFieldLabel(f.field))
   const summarySentence =
     fieldLabels.length === 0
-      ? `Reported scores diverge by ${formatSignalNumber(magnitude)}, above the ${formatSignalNumber(threshold)} threshold. The setup difference is not labelled.`
+      ? `Reported scores diverge by ${formatSignalNumber(magnitude)}, above the ${formatSignalNumber(threshold)} threshold. The setup difference is not labeled.`
       : `Reported scores diverge by ${formatSignalNumber(magnitude)} (threshold ${formatSignalNumber(threshold)}) because the runs differ on ${
           fieldLabels.length === 1
             ? fieldLabels[0]

content/tutorials/what-its-built-on.md

@@ -10,7 +10,7 @@ A schema for benchmark-level metadata: what a benchmark measures, its splits, in
 
 ## Every Eval Ever
 
-A run-level corpus of public evaluation results: `(model, benchmark, metric-path, value, source)` tuples extracted from papers, model cards, and leaderboards. It provides the raw rows that Evaluation Cards canonicalises and joins. [Every Eval Ever](https://evalevalai.com/every_eval_ever/) is a sister [EvalEval](https://evalevalai.com/) project.
+A run-level corpus of public evaluation results: `(model, benchmark, metric-path, value, source)` tuples extracted from papers, model cards, and leaderboards. It provides the raw rows that Evaluation Cards canonicalizes and joins. [Every Eval Ever](https://evalevalai.com/every_eval_ever/) is a sister [EvalEval](https://evalevalai.com/) project.
 
 ## IBM Risk Atlas alignment
 

lib/benchmark-tags.ts

@@ -24,18 +24,18 @@ import { inferTagsFromBenchmark } from "@/lib/benchmark-schema"
 
 const REF: Record<string, string[]> = categoriesJson as Record<string, string[]>
 
-// Two normalised lookup tables built once at module load. The first
+// Two normalized lookup tables built once at module load. The first
 // keeps spaces (so "MMLU Pro" still differs from "MMLUPro" if both
 // were ever in the file); the second strips everything non-alphanumeric
 // for a tolerant fallback ("ARC-C" ↔ "arc c" ↔ "arcc").
-const NORMALISED_LOOSE: Map<string, string[]> = new Map()
-const NORMALISED_TIGHT: Map<string, string[]> = new Map()
+const NORMALIZED_LOOSE: Map<string, string[]> = new Map()
+const NORMALIZED_TIGHT: Map<string, string[]> = new Map()
 
-function normaliseLoose(name: string): string {
+function normalizeLoose(name: string): string {
   return name.trim().toLowerCase().replace(/\s+/g, " ")
 }
 
-function normaliseTight(name: string): string {
+function normalizeTight(name: string): string {
   return name.toLowerCase().replace(/[^a-z0-9]/g, "")
 }
 
@@ -56,9 +56,9 @@ function stripParenSuffix(name: string): string {
 }
 
 for (const [name, tags] of Object.entries(REF)) {
-  NORMALISED_LOOSE.set(normaliseLoose(name), tags)
-  const tight = normaliseTight(name)
-  if (tight && !NORMALISED_TIGHT.has(tight)) NORMALISED_TIGHT.set(tight, tags)
+  NORMALIZED_LOOSE.set(normalizeLoose(name), tags)
+  const tight = normalizeTight(name)
+  if (tight && !NORMALIZED_TIGHT.has(tight)) NORMALIZED_TIGHT.set(tight, tags)
 }
 
 /**
@@ -78,15 +78,15 @@ export function getBenchmarkTags(
 ): string[] {
   const names = candidates.filter((n): n is string => typeof n === "string" && n.trim().length > 0)
   for (const name of names) {
-    const loose = NORMALISED_LOOSE.get(normaliseLoose(name))
+    const loose = NORMALIZED_LOOSE.get(normalizeLoose(name))
     if (loose) return loose
-    const tight = NORMALISED_TIGHT.get(normaliseTight(name))
+    const tight = NORMALIZED_TIGHT.get(normalizeTight(name))
     if (tight) return tight
     const stripped = stripParenSuffix(name)
     if (stripped && stripped !== name) {
-      const loose2 = NORMALISED_LOOSE.get(normaliseLoose(stripped))
+      const loose2 = NORMALIZED_LOOSE.get(normalizeLoose(stripped))
       if (loose2) return loose2
-      const tight2 = NORMALISED_TIGHT.get(normaliseTight(stripped))
+      const tight2 = NORMALIZED_TIGHT.get(normalizeTight(stripped))
       if (tight2) return tight2
     }
   }
@@ -107,8 +107,8 @@ export function getBenchmarkTags(
 export function hasCuratedTags(...candidates: Array<string | null | undefined>): boolean {
   for (const name of candidates) {
     if (typeof name !== "string" || !name.trim()) continue
-    if (NORMALISED_LOOSE.has(normaliseLoose(name))) return true
-    if (NORMALISED_TIGHT.has(normaliseTight(name))) return true
+    if (NORMALIZED_LOOSE.has(normalizeLoose(name))) return true
+    if (NORMALIZED_TIGHT.has(normalizeTight(name))) return true
   }
   return false
 }