Derogating Innocent Victims: The Effects of Relative Versus Absolute Character Judgments
Bibliographic record
Abstract
Drawing on just-world theory and research into the suppression and justification of prejudice, we propose that the use of relative compared with absolute measures of an innocent victim's character enables observers to derogate the victim without transparently violating social norms or values proscribing derogation. In Study 1, we found that positive feelings expressed toward victims mirrored social norms proscribing negative reactions toward them. In Studies 2a, 2b, and 3, innocent victims were evaluated more negatively when ratings were made using relative (i.e., compared with evaluations of the average student or the self) versus absolute scales. In Study 4, this effect of scale type on derogation was stronger for people higher in the motivation to avoid prejudiced reactions to victims. Relative judgments seem to allow individuals to enact their counternormative motivation to derogate the victim under the cover of ambiguity and ostensibly rationally motivated social comparison processes.
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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 | 0.008 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".