Greater Traditionalism Predicts COVID-19 Precautionary Behaviors Across 27 Societies
Bibliographic record
Abstract
People vary in the extent to which they embrace their society’s traditions, impacting a range of social and political phenomena. People also vary in the degree to which they perceive disparate dangers as salient and necessitating a response. Over evolutionary time, traditions likely regularly offered direct and indirect avenues for addressing hazards; consequently, via multiple possible pathways, orientations toward tradition and toward danger may have become associated. Emerging research documents connections between individual differences in traditionalism and variation in threat responsivity in general, and pathogen-avoidance motivations in particular. Importantly, because threat-mitigating behaviors can conflict with competing priorities, the precise associations between traditionalism and pathogen avoidance likely depend on contextually contingent costs and benefits. The COVID-19 pandemic requires individuals to make decisions about consequential and costly pathogen-avoidance behaviors that can clash with other priorities. The pandemic therefore provides a real-world setting in which to test the posited relationship between traditionalism and pathogen avoidance across socio-political contexts. Across 27 societies (N = 7,844), we find that costly COVID-19-avoidance behaviors positively correlate with greater endorsement of traditional norms and values in a majority of countries. Accounting for the conflict that arises in some societies between public health precautions and competing priorities, such as the exercise of personal liberties, reveals a consistent relationship between traditionalism and COVID-19 precautions across an even wider range of social and cultural contexts. These findings support the thesis that traditionalism is associated with an enhanced tendency to attend to hazards.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".