Victims’ Race and Sex Leads to Eyewitness Misidentification of Perpetrator’s Phenotypic Stereotypicality
Bibliographic record
Abstract
Eyewitness misidentification is the primary cause of wrongful convictions in North America. Discovering a discernible pattern to these errors is a critical step toward creating procedures that reduce the occurrence of these tragic mistakes. To these ends, we hypothesized that both the victims’ race and the victims’ sex may impact eyewitness identification for perpetrators of certain crime types. In two experiments, we demonstrated that a Black male drive-by shooter’s level of phenotypic stereotypicality is accurately identified by eyewitnesses only when the victims are Black males. Specifically, when eyewitnesses believe the victims are White or female, the drive-by shooter’s level of Black phenotypic stereotypicality is falsely elevated. In contrast, when a Black male perpetrator is suspected of committing a stereotypically non-Black crime (i.e., serial killing), the perpetrator’s level of phenotypic stereotypicality is accurately identified regardless of the victims’ race or sex.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".