What Influences Shooter Bias? The Effects of Suspect Race, Neighborhood, and Clothing on Decisions to Shoot
Bibliographic record
Abstract
Abstract Police shooting deaths of unarmed Blacks and African Americans led to psychological research on the influence of racial stereotypes on decisions to shoot, an effect called shooter bias. This article investigates how contextual cues signaling threat or safety interact with the race of the target to moderate shooter bias. Across two experimental studies using a first person shooter task, participants viewed Black or White male targets who held either a neutral (wallet or cellphone) or dangerous (gun) object. Study 1 manipulated the perceived safety or threat associated with the neighborhood context these shooting decisions occurred in, and Study 2 manipulated the perceived safety or threat associated with the targets’ clothing. Participants made quick decisions to “shoot” or “not shoot” the presented target, with error rates serving as the dependent variable. Across both studies, results confirmed that racial bias in shooting decisions against Blacks was present in perceived threatening neighborhoods and in perceived threatening clothing, and it was reduced in perceived safe neighborhoods and when wearing perceived safe clothing. Results help to identify contextual factors that may lead to mistaken shooting decisions, which can be used to improve police training and decision making to reduce bias.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".