Unethical and inept? The influence of moral information on perceptions of competence.
Bibliographic record
Abstract
While moral character heavily influences global evaluations of others (Goodwin, Piazza, & Rozin, 2014), its causal effect on perceptions of others' competence (i.e., one's knowledge, skills, and abilities) is less clear. We found that people readily use information about another's morality when judging their competence, despite holding folk intuitions that these domains are independent. Across 6 studies (n = 1,567), including 2 preregistered experiments, participants judged targets who committed hypothetical transgressions (Studies 1 and 3), cheated on lab tasks (Study 2), acted selfishly in economic games (Study 4), and received low morality ratings from coworkers (Study 5 and 6) as less competent than control or moral targets. These findings were specific to morality and were not the result of incidentally manipulating impressions of warmth (Study 4), nor were they fully explained by a general halo effect (Studies 2 and 3). We hypothesized that immoral targets are seen as less competent because their immoral actions led them to be viewed as low in social intelligence. Studies 4 and 5 supported this prediction, demonstrating that social intelligence was a more reliable mediator than perceptions of self-control or general intelligence. An experimental test of this mediation argument found that presenting targets as highly socially intelligent eliminated the negative effect of immoral information on judgments of competence (Study 6). These results suggest that information about a person's moral character readily influences perceptions of their competence. (PsycINFO Database Record
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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 | 0.004 | 0.038 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".