Racial bias in judgments of physical size and formidability: From size to threat.
Bibliographic record
Abstract
Black men tend to be stereotyped as threatening and, as a result, may be disproportionately targeted by police even when unarmed. Here, we found evidence that biased perceptions of young Black men's physical size may play a role in this process. The results of 7 studies showed that people have a bias to perceive young Black men as bigger (taller, heavier, more muscular) and more physically threatening (stronger, more capable of harm) than young White men. Both bottom-up cues of racial prototypicality and top-down information about race supported these misperceptions. Furthermore, this racial bias persisted even among a target sample from whom upper-body strength was controlled (suggesting that racial differences in formidability judgments are a product of bias rather than accuracy). Biased formidability judgments in turn promoted participants' justifications of hypothetical use of force against Black suspects of crime. Thus, perceivers appear to integrate multiple pieces of information to ultimately conclude that young Black men are more physically threatening than young White men, believing that they must therefore be controlled using more aggressive measures. (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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".