Cross-Cultural Differences in a Global “Survey of World Views”
Bibliographic record
Abstract
We know that there are cross-cultural differences in psychological variables, such as individualism/collectivism. But it has not been clear which of these variables show relatively the greatest differences. The Survey of World Views project operated from the premise that such issues are best addressed in a diverse sampling of countries representing a majority of the world’s population, with a very large range of item-content. Data were collected online from 8,883 individuals (almost entirely college students based on local publicizing efforts) in 33 countries that constitute more than two third of the world’s population, using items drawn from measures of nearly 50 variables. This report focuses on the broadest patterns evident in item data. The largest differences were not in those contents most frequently emphasized in cross-cultural psychology (e.g., values, social axioms, cultural tightness), but instead in contents involving religion, regularity-norm behaviors, family roles and living arrangements, and ethnonationalism. Content not often studied cross-culturally (e.g., materialism, Machiavellianism, isms dimensions, moral foundations) demonstrated moderate-magnitude differences. Further studies are needed to refine such conclusions, but indications are that cross-cultural psychology may benefit from casting a wider net in terms of the psychological variables of focus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".