Differentially dangerous? Phenotypic racial stereotypicality increases implicit bias among ingroup and outgroup members
Bibliographic record
Abstract
This article investigates whether within-group differences in perceived phenotypic racial stereotypicality can exacerbate implicit racial stereotyping for Blacks among both ingroup and outgroup members. Two studies with non-Black (Study 1) and Black (Study 2) participants confirmed that high stereotypical (HS) Black targets (i.e., those with darker skin, broader noses and fuller lips) elicited stronger implicit bias in split-second “shoot/don’t shoot” situations than low stereotypical (LS) Black targets or White targets. Specifically, a lower shooting criterion was adopted for HS Black targets, indicating a greater willingness to shoot HS Black targets, resulting in more pronounced bias. Results suggest that the perceived phenotypic racial stereotypicality of Black targets can increase the accessibility of stereotypes linking Blacks with danger, which intensifies racial bias. Further, the article provides the first empirical evidence that stereotypicality biases operate at implicit levels among Blacks when evaluating ingroup members. The implications for stereotypicality research and policing are 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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".