Lineup identification by children: Effects of clothing bias.
Bibliographic record
Abstract
This study examined effects of clothing cues on children's identification accuracy from lineups. Four- to 14-year-olds (n = 228) saw 12 video clips of individuals, each wearing a distinctly colored shirt. After watching each clip children were presented with a target-present or target-absent photo lineup. Three clothing conditions were included. In 2 conditions all lineup members wore the same colored shirt; in the third, biased condition, the shirt color of only one individual matched that seen in the preceding clip (the target in target-present trials and the replacement in target-absent trials). Correct identifications of the target in target-present trials were most frequent in the biased condition, whereas in target-absent trials the biased condition led to more false identifications of the target replacement. Older children were more accurate than younger children, both in choosing the target from target-present lineups and rejecting target-absent lineups. These findings suggest that a simple clothing cue such as shirt color can have a significant impact on children's lineup identification accuracy.
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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 | 0.016 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".