Taking the Watchdog Off Its Leash: Personal Prejudices and Situational Motivations Jointly Predict Derogation of a Stigmatized Source
Bibliographic record
Abstract
Lower- and higher-prejudiced individuals may strategically derogate negatively stereotyped individuals. Regardless of degree of prejudice, participants with a directional goal to discredit a threatening message and its source were more likely to do so when the source belonged to a negatively stereotyped group. They also were less persuaded by that stigmatized source. When this directional goal was negated by making the message nonthreatening, lower-prejudiced individuals evaluated the stigmatized and nonstigmatized sources, and their messages, similarly and were equally persuaded by both sources. When an accuracy goal was simultaneously introduced, lower-prejudice participants again rated the stigmatized and nonstigmatized sources comparably yet continued to derogate the stigmatized speaker's message and were less persuaded by him. Removing the directional goal or adding the accuracy goal did not affect higher-prejudiced participants' evaluations. The importance of examining situational goals and individual differences when studying biased responding is discussed.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".