When actions speak volumes: The role of inferences about moral character in outrage over racial bigotry
Bibliographic record
Abstract
Inferences about moral character may often drive outrage over symbolic acts of racial bigotry. Study 1 demonstrates a theoretically predicted dissociation between moral evaluations of an act and the person who carries out the act. Although Americans regarded the private use of a racial slur as a less blameworthy act than physical assault, use of a slur was perceived as a clearer indicator of poor moral character. Study 2 highlights the dynamic interplay between moral judgments of acts and persons, demonstrating that first making person judgments can bias subsequent act judgments. Privately defacing a picture of Martin Luther King, Jr. led to greater moral condemnation of the agent than of the act itself only when the behavior was evaluated first. When Americans first made character judgments, symbolically defacing a picture of the civil rights leader was significantly more likely to be perceived as an immoral act. These studies support a person-centered account of outrage over bigotry and demonstrate that moral evaluations of acts and persons converge and diverge under theoretically meaningful circumstances.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".