Pathogens and Politics: Further Evidence That Parasite Prevalence Predicts Authoritarianism
Bibliographic record
Abstract
According to a "parasite stress" hypothesis, authoritarian governments are more likely to emerge in regions characterized by a high prevalence of disease-causing pathogens. Recent cross-national evidence is consistent with this hypothesis, but there are inferential limitations associated with that evidence. We report two studies that address some of these limitations, and provide further tests of the hypothesis. Study 1 revealed that parasite prevalence strongly predicted cross-national differences on measures assessing individuals' authoritarian personalities, and this effect statistically mediated the relationship between parasite prevalence and authoritarian governance. The mediation result is inconsistent with an alternative explanation for previous findings. To address further limitations associated with cross-national comparisons, Study 2 tested the parasite stress hypothesis on a sample of traditional small-scale societies (the Standard Cross-Cultural Sample). Results revealed that parasite prevalence predicted measures of authoritarian governance, and did so even when statistically controlling for other threats to human welfare. (One additional threat-famine-also uniquely predicted authoritarianism.) Together, these results further substantiate the parasite stress hypothesis of authoritarianism, and suggest that societal differences in authoritarian governance result, in part, from cultural differences in individuals' authoritarian personalities.
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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 | 0.007 |
| 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 | 0.001 |
| 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".