How sure are you that this is the man you saw? Child witnesses can use confidence judgments to identify a target.
Bibliographic record
Abstract
We tested whether an alternative lineup procedure designed to minimize problematic influences (e.g., metacognitive development) on decision criteria could be effectively used by children and improve child eyewitness identification performance relative to a standard identification task. Five hundred sixteen children (6- to 13-year-olds) watched a video of a target reading word lists and, the next day, made confidence ratings for each lineup member or standard categorical decisions for 8 lineup members presented sequentially. Two algorithms were applied to classify confidence ratings into categorical decisions and facilitate comparisons across conditions. The classification algorithms produced accuracy rates for the confidence rating procedure that were comparable to the categorical procedure. These findings demonstrate that children can use a ratings-based procedure to discriminate between previously seen and unseen faces. In turn, this invites more nuanced and empirical consideration of ratings-based identification evidence as a probabilistic index of guilt that may attenuate problematic social influences on child witnesses' decision criteria. (PsycINFO Database Record
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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 | 0.004 | 0.057 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".