Eyewitness identifications are affected by stereotypes about a suspect’s level of perceived stereotypicality
Bibliographic record
Abstract
Mistaken identifications are the primary cause of wrongful convictions. Though studies have examined when these errors are likely to occur, none have specified whom these errors are most likely to affect. We address this oversight by arguing that the type of crime committed affects whom eyewitnesses misidentify. Study 1 demonstrated that people have stereotypes about a perpetrator’s appearance that vary by the crime committed. Study 2 showed that these stereotypes affect identifications in a stereotype-consistent manner—participants who believed they saw a target accused of a stereotypically Black crime remembered him as being higher on perceived stereotypicality (viz., having more Afrocentric features) than did participants who believed they saw a target accused of a stereotypically White crime. This finding was replicated in Study 3 using a different pair of crimes. These studies demonstrate that the type of crime committed systematically affects whom eyewitnesses mistakenly identify.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".