Predictive modeling of religiosity, prosociality, and moralizing in 295,000 individuals from European and non-European populations
Bibliographic record
Abstract
Abstract Why do moral religions exist? An influential psychological explanation is that religious beliefs in supernatural punishment is cultural group adaptation enhancing prosocial attitudes and thereby large-scale cooperation. An alternative explanation is that religiosity is an individual strategy that results from high level of mistrust and the need for individuals to control others’ behaviors through moralizing. Existing evidence is mixed but most works are limited by sample size and generalizability issues. The present study overcomes these limitations by applying k-fold cross-validation on multivariate modeling of data from >295,000 individuals in 108 countries of the World Values Surveys and the European Value Study. First, this methodology reveals no evidence that European and non-European religious people invest more in collective actions and are more trustful of unrelated conspecifics. Instead, the individuals’ level of religiosity is found to be weakly but positively associated with social mistrust and negatively associated with the production of behaviors, which benefit unrelated members of the large-scale community. Second, our models show that individual variation in religiosity is well explained by the interaction of increased levels of social mistrust and increased needs to moralize other people’s sexual behaviors. Finally, stratified k-fold cross-validation demonstrates that the structures of these association patterns are robust to sampling variability and reliable enough to generalize to out-of-sample data.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".