A Repeated Forced-Choice Lineup Procedure: Examining the Impact on Child and Adult Eyewitnesses
Bibliographic record
Abstract
In two experiments and one follow-up analysis, I examined the impact of using a repeated force-choice (RFC) lineup procedure with child and adult eyewitnesses. The RFC procedure divides the identification task into a series of exhaustive binary comparisons (i.e., round-robin design) and, in doing so, provides information about (a) who the witness believes is the suspect (if any) and, (b) additional information about how each face in the lineup matches the witness’ memory of a target, relative to every other face. Results from Experiment 1 indicate that younger children (6-to-8-year-olds) struggled with the RFC procedure, while older children (9-to-11-year-olds) performed at least as well with the RFC procedure as with a simultaneous procedure. In Experiment 2, the comparable performance in the simultaneous and RFC procedures was replicated with adult eyewitnesses. Follow-up analyses examined the additional information provided by the RFC in Experiments 1 and 2 and found evidence that witnesses’ patterns of responding during the RFC procedure can be used to estimate selection bias or memory strength associated with an individual witness’ lineup decision.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".