Eyewitness Identification and Distinctive Features: When Similarity Matters
Bibliographic record
Abstract
In four experiments, we investigated theoretical and practical issues around eyewitness identification accuracy and confidence for tattooed suspects. We varied how tattoos were treated in lineups (Experiments 1 and 2) and the match between the suspect’s tattoo the perpetrator’s tattoo (Experiments 3 and 4). We replicated the finding that modifying lineup photographs to prevent a tattooed suspect from standing out mitigates the risk of innocent suspect identifications. We also demonstrated that sequential lineups (cf. simultaneous) do not mitigate the risk of biased lineups when the suspect stands out because of a tattoo. Contrary to previous research in which biased lineups did not impact correct identification rates differentially by lineup type, we found that biased lineups decreased correct identifications in sequential, but not simultaneous, lineups. Additionally, we found that the tattoo worn by an innocent suspect need not be identical to that of the perpetrator—similar placement and designs also inflate innocent suspect identifications, although a tattoo in a different location with a different design protected innocent suspects. Finally, our data indicate that when researching distinctive marks in lineups, researchers should request descriptions from the eyewitness-participants following the mock crime in order to determine whether the witness noticed the distinctive mark.
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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 | 0.017 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".