Seeing Faces Differently: Assessing the Influence of Children's Perceived Similarity on Eyewitness Identification Accuracy
Bibliographic record
Abstract
ABSTRACT We investigated whether using children's perceived similarity to construct lineups changed children's identification performance. After a pilot showing that children rate suspects and fillers as more similar than adults do, we ran three experiments with child eyewitnesses (ages 6–11, younger group 6–8; older, 9–11) and an adult comparison group (18‐ to 58‐years‐old; Experiment 3). We analyzed accuracy, discriminability, confidence‐accuracy calibration, and decision patterns (suspect identification, filler identification, or rejection), as a function of both target presence and lineup creator (adult‐ or child‐created). Experiments 1 and 2 found that child‐created lineups improved children's pattern of responding to suggest better discriminability and better confidence‐accuracy calibration. In Experiment 3, we extended the design to include an adult sample, finding no effect of lineup type for adult witnesses and limited benefits for children. These results suggest that age‐matched similarity information can improve children's lineup performance under some conditions, but the benefits are not universal.
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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 | 0.001 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".