Lineup size influences voice identification accuracy
Bibliographic record
Abstract
Abstract We investigated the effects of lineup size, target presence, and target sex on voice identification accuracy. In Study 1, participants identified a female target from 1‐, 6‐, or 10‐voice target present or target absent lineups. Results demonstrated increasing accuracy with smaller target absent lineups. One‐voice lineups led to higher rates of innocent suspect identifications than 10‐voice lineups. In Study 2, participants identified a male or female target from 1‐, 6‐, or 10‐voice target present or target absent lineups. Results revealed increased correct identifications in 1‐ and 6‐voice and increased innocent suspect identifications in 1‐voice lineups, compared to 10‐voice lineups. Female lineups resulted in higher accuracy in target present lineups yet increased rates of innocent suspect identifications. Our findings indicate 1‐voice lineups result in both increased accuracy and increased innocent suspect identifications, a trade‐off which may not be advisable. Future research should further explore the impact of multi‐voice lineups on accuracy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 0.034 |
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; both teacher heads agree on what is shown here.
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".