Accordance and conflict between religious and scientific precautions against COVID-19 in 27 societies
Bibliographic record
Abstract
Meaning-making systems underlie perceptions of the efficacy of threat-mitigating behaviors. Religion and science both offer threat mitigation, yet these two meaning-making systems are often considered incompatible. Do such epistemological conflicts swamp the desire to employ diverse precautions against threats? Or do individuals – particularly individuals who are highly reactive to threats – hedge their bets by using multiple threat-mitigating practices despite their potential epistemological incompatibility? Complicating this question, perceptions of conflict between religion and science likely vary across cultures; likewise, pragmatic features of precautions prescribed by some religions make them incompatible with some scientifically-based precautions. The COVID-19 pandemic elicited diverse precautionary behaviors, and thus provided an opportunity to investigate these questions. Across 27 societies from five continents (N = 7,844), in the majority of countries, individuals’ practice of religious precautions such as prayer correlates positively with their use of scientifically-based precautions. Prior work indicates that greater adherence to tradition likely reflects greater reactivity to threats. Unsurprisingly given associations between many traditions and religion, we find that valuing tradition is predictive of employing religious precautions. However, consonant with its association with threat reactivity, we also find that traditionalism predicts adherence to public health precautions – a pattern that underscores threat-avoidant individuals’ apparent tolerance for epistemological conflict in pursuit of safety.
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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 | 0.002 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".