Pathogen prevalence predicts human cross-cultural variability in individualism/collectivism
Bibliographic record
Abstract
Pathogenic diseases impose selection pressures on the social behaviour of host populations. In humans (Homo sapiens), many psychological phenomena appear to serve an antipathogen defence function. One broad implication is the existence of cross-cultural differences in human cognition and behaviour contingent upon the relative presence of pathogens in the local ecology. We focus specifically on one fundamental cultural variable: differences in individualistic versus collectivist values. We suggest that specific behavioural manifestations of collectivism (e.g. ethnocentrism, conformity) can inhibit the transmission of pathogens; and so we hypothesize that collectivism (compared with individualism) will more often characterize cultures in regions that have historically had higher prevalence of pathogens. Drawing on epidemiological data and the findings of worldwide cross-national surveys of individualism/collectivism, our results support this hypothesis: the regional prevalence of pathogens has a strong positive correlation with cultural indicators of collectivism and a strong negative correlation with individualism. The correlations remain significant even when controlling for potential confounding variables. These results help to explain the origin of a paradigmatic cross-cultural difference, and reveal previously undocumented consequences of pathogenic diseases on the variable nature of human societies.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".