Who Is Your Biggest Critic? Cultural Variation in Moral Judgments of the Self and Others
Bibliographic record
Abstract
People are motivated to punish others who commit immoral actions when they believe the person willingly committed such an act. Compared with European American individuals, East Asian individuals are more punitive of wrongdoings, yet are less likely to attribute actions to the person. Here, we drew on research in cultural psychology to test the prediction that Chinese individuals are more punitive in part because they are more self-critical than European American individuals. This prediction would imply that cultural differences in punishment are most pronounced in judgments of oneself (vs. others) and largely driven by a difference in self-enhancement motives. To test this prediction, we conducted two studies, where 1,563 participants imagined immoral (vs. moral) actions performed by themselves or others. We then measured self-enhancement (how much participants perceived the immoral act impacts self-esteem) and attributions (how much participants perceived the immoral act is due to the person). As predicted, Chinese individuals punished immoral behavior more than European American individuals, which was explained by Chinese individuals being less self-enhancing, as indicated by a greater perception that immoral actions will negatively impact their self-esteem. Dispositional attributions predicted punishment regardless of culture. This work highlights how cultural differences in self-enhancement are key to understanding moral judgments and their cultural variation.
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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 | 0.003 | 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 | 0.001 | 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) | 0.001 | 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".