Crime Type, Perceived Stereotypicality, and Memory Biases: A Contextual Model of Eyewitness Identification
Bibliographic record
Abstract
Summary The fallibility of eyewitness identifications is well documented. Nevertheless, research has yet to assess the possibility that the type of crime committed systematically influences who eyewitnesses mistakenly identify. We address this oversight by presenting a contextual model of eyewitness identification (CMEI). The CMEI asserts that discrete crimes automatically activate distinct stereotypes about a perpetrator's appearance. Depending on the congruence between these stereotypes and the perpetrator's actual appearance, eyewitnesses will remember the perpetrator as appearing more (or less) representative of his or her group (i.e., higher or lower on perceived stereotypicality). Estimator and system variables are posited to affect identifications at different stages of the identification process. The literatures on stereotype activation, perceived stereotypicality, and stereotype‐consistent memory biases are reviewed to support the CMEI. Our conceptual integration provides a model of eyewitness identification that explains when mistaken identifications are likely to occur and who they are likely to affect. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".