Children's and adults' eyewitness identification accuracy when a culprit changes his appearance: Comparing simultaneous and elimination lineup procedures
Bibliographic record
Abstract
Adults' ( N = 239) and children's ( N = 177, age range 8–13 years) identification abilities were examined when a culprit underwent a change in appearance following the commission of a crime. Simultaneous and elimination lineup procedures were compared to determine the reliability of each under ‘change in appearance’ conditions. Participants viewed a staged, videotaped theft and then examined a target‐present or ‐absent lineup. Correct identifications (target‐present lineups) decreased following a change in appearance regardless of age of witness and lineup procedure. Children's correct rejection rates (target‐absent lineups) were lower than those of adults. The elimination procedure compared with the simultaneous procedure was more effective at increasing correct rejections when the lineup members matched the culprit's appearance for children and adults. When lineup members did not match the culprit's appearance, correct rejection rates were similar across the two identification procedures for both aged groups.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".