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 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 | 0.038 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".