When the Mighty Fall: Moral Outrage Toward Powerful Perpetrators
Bibliographic record
Abstract
Past work demonstrates that powerful individuals are more inclined to act unethically (e.g., Gruenfeld et al., 2008). As a result, people are routinely confronted with powerful transgressors. Power is a potent force in perception (e.g., Keltner et al., 2008) and, therefore, I investigate how a perpetrator’s power will impact people’s moral outrage towards bad behavior. Powerful transgressors, due to their control over valued resources and social influence, can cause greater harm to victims and are more likely to encourage unethical group norms, compared to less powerful transgressors. Therefore, across six studies, I hypothesized that powerful perpetrators would elicit greater moral outrage than less powerful perpetrators. Participants reported greater moral outrage toward powerful transgressors who acted unethically in hypothetical scenarios (pre-registered Study 1 and Study 2), real stakes economic games (Study 3), live interactions in a laboratory (Study 4), and recalled events (Study 5). This robust effect held in contexts when the perpetrator’s power was not directly relevant, regardless of the victim’s power, and generalized across a variety of unethical acts. It was mediated by perceptions that powerful transgressors inflicted greater harm to victims and encouraged unethical group norms (Studies 2 and 3). However, this effect was moderated by important individual differences and contextual variables. Specifically, this effect was eliminated or reversed for participants who heavily endorse system-justifying ideologies (Study 5) and towards admired leaders within one’s own group (Study 6). My findings highlight the importance of social features like power in influencing moral judgment, suggesting that moral perception does not occur in a social vacuum.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".