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Record W4366214988 · doi:10.31234/osf.io/8wr5d

More educated people are more WEIRD: Higher education predicts global cultural similarity to WEIRD countries

2023· preprint· en· W4366214988 on OpenAlexaff
Cindel White, Michael Muthukrishna

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsYork University
FundersTempleton World Charity FoundationJohn Templeton Foundation
KeywordsSimilarity (geometry)Computer scienceImage (mathematics)Artificial intelligence

Abstract

fetched live from OpenAlex

Behavioral scientists increasingly recruit participants from less-WEIRD societies, but continue to oversample highly educated people, such as university students and online workers. Here we test how representative highly educated individuals are of the world’s cultural diversity. We used the cultural fixation index (CFST) to measure cultural distance between people with high and low education in 95 countries (N=268,992), across beliefs, values, and behaviors assessed by the World Values Survey (2005–2022). We find that more highly educated people are significantly more culturally similar to WEIRD countries, including the United States, Anglosphere, and Western Europe. Education did not predict cultural similarities to other large, culturally and politically influential countries, such as China, Russia, and India. Income and self-reported subjective status did not show the same pattern of WEIRD cultural similarity. Our results are consistent with hypotheses linking formal education with Western culture and suggest that cross-cultural samples of university-educated individuals underrepresent the world’s cultural variation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.119
GPT teacher head0.428
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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