Outsourcing punishment to God: beliefs in divine control reduce earthly punishment
Bibliographic record
Abstract
The sanctioning of norm-transgressors is a necessary--though often costly--task for maintaining a well-functioning society. Prior to effective and reliable secular institutions for punishment, large-scale societies depended on individuals engaging in 'altruistic punishment'--bearing the costs of punishment individually, for the benefit of society. Evolutionary approaches to religion suggest that beliefs in powerful, moralizing Gods, who can distribute rewards and punishments, emerged as a way to augment earthly punishment in large societies that could not effectively monitor norm violations. In five studies, we investigate whether such beliefs in God can replace people's motivation to engage in altruistic punishment, and their support for state-sponsored punishment. Results show that, although religiosity generally predicts higher levels of punishment, the specific belief in powerful, intervening Gods reduces altruistic punishment and support for state-sponsored punishment. Moreover, these effects are specifically owing to differences in people's perceptions that humans are responsible for punishing wrongdoers.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".