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Record W4399292981 · doi:10.1177/00220221241255673

Who Is Your Biggest Critic? Cultural Variation in Moral Judgments of the Self and Others

2024· article· en· W4399292981 on OpenAlexaff
Cristina Salvador, Cindel White, Ting Ai

Bibliographic record

VenueJournal of Cross-Cultural Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsYork University
Fundersnot available
KeywordsVariation (astronomy)PsychologySocial psychologySociologyAstronomy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.121
GPT teacher head0.476
Teacher spread0.355 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2024
Admission routes1
Has abstractyes

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