Estimation of eyewitness error rates in fair and biased lineups.
Bibliographic record
Abstract
OBJECTIVE: The risk of mistaken identification for innocent suspects in lineups can be estimated by correcting the overall error rate by the number of people in the lineup. We compared this nominal size correction to a new effective size correction, which adjusts the error rate for the number of plausible lineup members. HYPOTHESES: We hypothesized that (a) increasing lineup bias would increase misidentifications of a designated innocent suspect; (b) with the effective size correction, increasing lineup bias would also increase the estimate of innocent-suspect misidentifications; and (c) with the nominal size correction, lineup bias would have no effect on the estimate of innocent-suspect misidentifications. METHOD: = 686, 405, and 1,531, respectively), participants observed a staged crime and completed a fair or biased lineup. RESULTS: = 0.84, 95% CI [0.60, 1.18]. CONCLUSIONS: Most lineups include a combination of plausible and implausible lineup members. Contrary to the nominal size correction, which ignores implausible lineup members, the effective size correction is sensitive to implausible lineup members and accounts for lineup bias when estimating the risk of innocent suspect misidentifications. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.000 |
| 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 | 0.000 |
| 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".