respect_of_counterarguments_1900_2021 stringclasses 13
values | unnamed_1 float64 1.9k 2.02k ⌀ | unnamed_2 float64 -2.23 3.01 ⌀ | esa_source stringclasses 1
value | esa_processed stringdate 2026-04-28 00:00:00 2026-04-28 00:00:00 |
|---|---|---|---|---|
Botswana | 1,922 | -1.94 | HDX | 2026-04-28 |
Cape Verde | 1,995 | 1.546 | HDX | 2026-04-28 |
Sudan | 1,942 | -0.489 | HDX | 2026-04-28 |
Senegal | 1,997 | 1.318 | HDX | 2026-04-28 |
Cape Verde | 1,997 | 1.546 | HDX | 2026-04-28 |
Senegal | 1,994 | 1.318 | HDX | 2026-04-28 |
Ethiopia | 2,017 | -0.237 | HDX | 2026-04-28 |
Kenya | 1,945 | -0.902 | HDX | 2026-04-28 |
Cape Verde | 2,001 | 1.546 | HDX | 2026-04-28 |
Kenya | 1,943 | -0.902 | HDX | 2026-04-28 |
Botswana | 1,910 | -1.94 | HDX | 2026-04-28 |
Cape Verde | 1,962 | -0.457 | HDX | 2026-04-28 |
Cape Verde | 2,020 | 0.675 | HDX | 2026-04-28 |
Kenya | 1,992 | 0.821 | HDX | 2026-04-28 |
Kenya | 1,997 | 1.382 | HDX | 2026-04-28 |
Nigeria | 2,002 | 1.189 | HDX | 2026-04-28 |
Botswana | 1,933 | -1.94 | HDX | 2026-04-28 |
Ethiopia | 1,912 | -1.503 | HDX | 2026-04-28 |
Zambia | 1,960 | -1.494 | HDX | 2026-04-28 |
Sudan | 1,923 | -0.489 | HDX | 2026-04-28 |
Sudan | 1,963 | -1.656 | HDX | 2026-04-28 |
Zambia | 1,938 | -1.494 | HDX | 2026-04-28 |
Senegal | 1,995 | 1.318 | HDX | 2026-04-28 |
Tanzania | 1,937 | -1.871 | HDX | 2026-04-28 |
Benin | 1,993 | 2.498 | HDX | 2026-04-28 |
Cape Verde | 1,999 | 1.546 | HDX | 2026-04-28 |
Tanzania | 1,975 | 0.35 | HDX | 2026-04-28 |
Kenya | 2,019 | 1.751 | HDX | 2026-04-28 |
Ethiopia | 1,970 | -1.34 | HDX | 2026-04-28 |
Botswana | 1,931 | -1.94 | HDX | 2026-04-28 |
South Africa | 1,918 | 0.497 | HDX | 2026-04-28 |
Senegal | 1,937 | -1.132 | HDX | 2026-04-28 |
Nigeria | 2,009 | 1.189 | HDX | 2026-04-28 |
Cape Verde | 1,961 | -0.457 | HDX | 2026-04-28 |
Kenya | 1,952 | -0.902 | HDX | 2026-04-28 |
Ethiopia | 1,925 | -1.503 | HDX | 2026-04-28 |
Ethiopia | 1,951 | -1.34 | HDX | 2026-04-28 |
Tanzania | 1,948 | -1.871 | HDX | 2026-04-28 |
Nigeria | 2,007 | 1.189 | HDX | 2026-04-28 |
Ethiopia | 2,012 | -0.319 | HDX | 2026-04-28 |
Ethiopia | 1,908 | -1.503 | HDX | 2026-04-28 |
Zambia | 1,987 | 0.047 | HDX | 2026-04-28 |
Sudan | 2,017 | -0.318 | HDX | 2026-04-28 |
South Africa | 1,951 | 0.411 | HDX | 2026-04-28 |
Sudan | 1,912 | -0.489 | HDX | 2026-04-28 |
South Africa | 1,966 | 0.302 | HDX | 2026-04-28 |
Sudan | 1,905 | -0.489 | HDX | 2026-04-28 |
Botswana | 1,953 | -0.859 | HDX | 2026-04-28 |
Zambia | 1,962 | -1.494 | HDX | 2026-04-28 |
South Africa | 2,001 | 1.431 | HDX | 2026-04-28 |
Sudan | 1,949 | -0.489 | HDX | 2026-04-28 |
Cape Verde | 1,947 | -0.457 | HDX | 2026-04-28 |
Sudan | 1,907 | -0.489 | HDX | 2026-04-28 |
Nigeria | 2,013 | 1.326 | HDX | 2026-04-28 |
Ethiopia | 1,966 | -1.34 | HDX | 2026-04-28 |
South Africa | 1,964 | 0.302 | HDX | 2026-04-28 |
Benin | 1,985 | 0.332 | HDX | 2026-04-28 |
Nigeria | 2,006 | 1.189 | HDX | 2026-04-28 |
Ethiopia | 1,921 | -1.503 | HDX | 2026-04-28 |
Tanzania | 1,941 | -1.871 | HDX | 2026-04-28 |
Kenya | 1,935 | -0.902 | HDX | 2026-04-28 |
Nigeria | 2,019 | 0.39 | HDX | 2026-04-28 |
Nigeria | 1,968 | -0.878 | HDX | 2026-04-28 |
Cape Verde | 1,943 | -0.457 | HDX | 2026-04-28 |
Cape Verde | 1,938 | -0.457 | HDX | 2026-04-28 |
Sudan | 2,006 | -1.203 | HDX | 2026-04-28 |
Zambia | 1,953 | -1.494 | HDX | 2026-04-28 |
Tanzania | 1,918 | -1.871 | HDX | 2026-04-28 |
Cape Verde | 1,919 | -0.457 | HDX | 2026-04-28 |
Cape Verde | 1,980 | 0.83 | HDX | 2026-04-28 |
Cape Verde | 1,992 | 1.546 | HDX | 2026-04-28 |
Benin | 1,956 | -0.221 | HDX | 2026-04-28 |
South Africa | 2,000 | 1.431 | HDX | 2026-04-28 |
Zambia | 1,949 | -1.494 | HDX | 2026-04-28 |
Nigeria | 1,945 | 0.462 | HDX | 2026-04-28 |
Sudan | 2,013 | -0.732 | HDX | 2026-04-28 |
South Africa | 1,916 | 0.497 | HDX | 2026-04-28 |
Ethiopia | 1,940 | -2.231 | HDX | 2026-04-28 |
Cape Verde | 1,986 | 0.83 | HDX | 2026-04-28 |
South Africa | 1,962 | 0.302 | HDX | 2026-04-28 |
Nigeria | 1,955 | 0.532 | HDX | 2026-04-28 |
Nigeria | 1,931 | 0.403 | HDX | 2026-04-28 |
South Africa | 1,956 | 0.411 | HDX | 2026-04-28 |
Senegal | 1,990 | 1.318 | HDX | 2026-04-28 |
Zambia | 1,928 | -1.494 | HDX | 2026-04-28 |
Senegal | 1,906 | -1.132 | HDX | 2026-04-28 |
Senegal | 1,950 | 0.236 | HDX | 2026-04-28 |
Ethiopia | 1,992 | -0.319 | HDX | 2026-04-28 |
Cape Verde | 1,981 | 0.83 | HDX | 2026-04-28 |
Ethiopia | 1,964 | -1.34 | HDX | 2026-04-28 |
Sudan | 2,019 | 0.502 | HDX | 2026-04-28 |
Sudan | 1,926 | -0.489 | HDX | 2026-04-28 |
Benin | 1,980 | 0.332 | HDX | 2026-04-28 |
Botswana | 1,904 | -1.94 | HDX | 2026-04-28 |
Tanzania | 2,020 | 1.131 | HDX | 2026-04-28 |
Tanzania | 1,958 | -1.871 | HDX | 2026-04-28 |
Botswana | 1,946 | -1.94 | HDX | 2026-04-28 |
Kenya | 1,960 | -0.762 | HDX | 2026-04-28 |
Cape Verde | 1,913 | -0.457 | HDX | 2026-04-28 |
Nigeria | 1,925 | 0.403 | HDX | 2026-04-28 |
Respect of Counterarguments (1900-2021)
Publisher: V-Dem Institute · Source: OpenAfrica · License: cc-by · Updated: 2023-01-27
Abstract
The variable Counter arguments scores denotes the best estimate of the extent to which political elites acknowledge and respect counterarguments when considering important policy changes.
Higher scores mean more respect.
Each row in this dataset represents tabular records. Data was last updated on OpenAfrica on 2023-01-27. Geographic scope: BENIN, BOTSWANA, CAPE-VERDE, ETHIOPIA, KENYA, NIGERIA, SENEGAL, SOUTH-AFRICA, and 4 others.
Curated into ML-ready Parquet format by Electric Sheep Africa.
Dataset Characteristics
| Domain | Humanitarian and development data |
| Unit of observation | Tabular records |
| Rows (total) | 1,302 |
| Columns | 5 (2 numeric, 3 categorical, 0 datetime) |
| Train split | 1,041 rows |
| Test split | 260 rows |
| Geographic scope | BENIN, BOTSWANA, CAPE-VERDE, ETHIOPIA, KENYA, NIGERIA, SENEGAL, SOUTH-AFRICA, and 4 others |
| Publisher | V-Dem Institute |
| OpenAfrica last updated | 2023-01-27 |
Variables
Outcome / Measurement — respect_of_counterarguments_1900_2021 (Benin, Botswana, Cape Verde).
Identifier / Metadata — unnamed_1 (range 1900.0–2021.0), unnamed_2 (range -2.231–3.012), esa_source (HDX), esa_processed (2026-04-28).
Quick Start
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-respect-of-counteraguments-1900-2021")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
print(train.shape)
train.head()
Schema
| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
respect_of_counterarguments_1900_2021 |
object | 0.1% | Benin, Botswana, Cape Verde |
unnamed_1 |
float64 | 0.2% | 1900.0 – 2021.0 (mean 1962.3156) |
unnamed_2 |
float64 | 0.2% | -2.231 – 3.012 (mean -0.1563) |
esa_source |
object | 0.0% | HDX |
esa_processed |
object | 0.0% | 2026-04-28 |
Numeric Summary
| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
unnamed_1 |
1900.0 | 2021.0 | 1962.3156 | 1962.0 |
unnamed_2 |
-2.231 | 3.012 | -0.1563 | -0.221 |
Curation
Raw data was downloaded from OpenAfrica via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 2 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
Limitations
- Data originates from V-Dem Institute and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- This dataset spans 12 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
- Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.
Citation
@dataset{openafrica_africa_respect_of_counteraguments_1900_2021,
title = {Respect of Counterarguments (1900-2021)},
author = {V-Dem Institute},
year = {2023},
url = {https://open.africa/dataset/respect-of-counteraguments-1900-2021},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.
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