Multiple independent lineups: a procedure for corroborating eyewitness identification evidence in children
Bibliographic record
Abstract
Child eyewitnesses exhibit problematic choosing on police lineups at a higher rate than adults (Fitzgerald, R. J., & Price, H. L. [2015]. Eyewitness identification across the life span: A meta-analysis of age differences. Psychological Bulletin, 141(6), 1228–1265), which is an issue as mistaken eyewitness testimony is a leading cause of wrongful convictions (National Registry of Exonerations. [2019]. Exoneration reports. National Registry of Exonerations. http://www.law.umich.edu/special/exoneration/Pages/about.aspx). This study examined a novel eyewitness reflector variable to use with children, the multiple independent lineup technique, to assess the likelihood of guilt. A total of 486 children (60% male, 39% female, and 1% other; Mage = 8.59) witnessed a live event and, the following day, engaged in a lineup identification task (i.e. single simultaneous face lineup or the multiple independent lineup technique). Largely, the results found support for the multiple independent lineup technique to help infer the likelihood of guilt. Specifically, the number of lineup decisions made could infer the likelihood of facial identification guilt. Interestingly, children of all ages performed similarly on the multiple independent lineup technique. The results also revealed that facial identification responses are similar between the two lineup conditions (i.e. single simultaneous lineup and multiple independent lineup technique). Overall, the multiple independent lineup technique is a simplistic tool that could provide legal decision-makers with additional information to help infer the likelihood of suspect guilt.
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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 | 0.001 |
| 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".