Eyewitness accuracy rates in police showup and lineup presentations: A meta-analytic comparison.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Meta-analysis is used to compare identification accuracy rates in showups and lineups. Eight papers were located, providing 12 tests of the hypothesis and including 3013 participants. Results indicate that showups generate lower choosing rates than lineups. In target present conditions, showups and lineups yield approximately equal hit rates, and in target absent conditions, showups produce a significantly higher level of correct rejections. False identification rates are approximately equal in showups and lineups when lineup foil choices are excluded from analysis. Dangerous false identifications are more numerous for showups when an innocent suspect resembles the perpetrator. Function of lineup foils, assessment strategies for false identifications, and the potential impact of biases in lineup practice are suggested as additional considerations in evaluation of showup versus lineup efficacy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it