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 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".