On the Origins of Cultural Differences in Conformity: Four Tests of the Pathogen Prevalence Hypothesis
Bibliographic record
Abstract
What are the origins of cultural differences in conformity? The authors deduce the hypothesis that these cultural differences may reflect historical variability in the prevalence of disease-causing pathogens: Where pathogens were more prevalent, there were likely to emerge cultural norms promoting greater conformity. The authors conducted four tests of this hypothesis, using countries as units of analysis. Results support the pathogen prevalence hypothesis. Pathogen prevalence positively predicts cultural differences in effect sizes that emerge from behavioral conformity experiments (r=.49, n=17) and in the percentage of the population who prioritize obedience (r=.48, n=83). Pathogen prevalence also negatively predicted two indicators of tolerance for nonconformity: within-country dispositional variability (r=-.48, n=33) and the percentage of the population who are left-handed (r=-.73, n=20). Additional analyses address plausible alternative causal explanations. Discussion focuses on plausible underlying mechanisms (e.g., genetic, developmental, cognitive).
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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 | 0.019 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".