Third-party punishment, vigilante justice, or karma? Understanding the dynamics of interpersonal and cosmic justice
Bibliographic record
Abstract
People around the world both engage in both interpersonal punishment and expect supernatural punishment of wrongdoers. That is, people will impose costs and withhold benefits from transgressors, and they expect bad things to happen to transgressors more often than to good people. Evolutionary theories have proposed that both interpersonal and supernatural justice beliefs result from similar motivations, cognitive mechanisms, and cultural evolutionary processes that bind human beings into cooperative groups. To explore these ideas, three preregistered studies (N = 3430) investigated situational factors and individual differences that shape reactions to interpersonal and supernatural justice. Perceived appropriateness of both interpersonal justice and supernatural justice depended on recipients' past moral actions, with more positive impressions when antisocial actions and bad outcomes befell previously antisocial victims. However, third-party interpersonal punishment was viewed far more negatively than interpersonal reprimands or supernatural punishments, especially when the potential punisher was unaware of the victim's past transgressions. Explicit belief in karma significantly moderated perceptions of harmful outcomes not caused by human agents, but karma belief was largely unrelated to perceptions of harm caused by humans. Together, results reveal distinct factors that predict judgments about interpersonal punishment and karmic punishments, and provide insight into the distinct dynamics of interpersonal and supernatural justice.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 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".