Does Moral Identity Effectively Predict Moral Behavior?: A Meta-Analysis
Bibliographic record
Abstract
This meta-analysis examined the relationship between moral identity and moral behavior. It was based on 111 studies from a broad range of academic fields including business, developmental psychology and education, marketing, sociology, and sport sciences. Moral identity was found to be significantly associated with moral behavior (random effects model, r = .22, p < .01, 95% CI [.19, .25]). Effect sizes did not differ for behavioral outcomes (prosocial behavior, avoidance of antisocial behavior, ethical behavior). Studies that were entirely based on self-reports yielded larger effect sizes. In contrast, the smallest effect was found for studies that were based on implicit measures or used priming techniques to elicit moral identity. Moreover, a marginally significant effect of culture indicated that studies conducted in collectivistic cultures yielded lower effect sizes than studies from individualistic cultures. Overall, the meta-analysis provides support for the notion that moral identity strengthens individuals’ readiness to engage in prosocial and ethical behavior as well as to abstain from antisocial behavior. However, moral identity fares no better as a predictor of moral action than other psychological constructs.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.037 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".