Identification Accuracy of Adolescent Eyewitnesses: The Role of Familiarity and Lineup Procedure
Bibliographic record
Abstract
The purpose of the current study was to examine the role of familiarity and lineup procedure on eyewitness identification accuracy.Familiarity was manipulated wherein adolescent participants (N = 623): (1) met with and directly interacted with a confederate, (2) indirectly interacted with a confederate, or (3) did not meet a confederate, before they viewed a crime video in which the confederate was the perpetrator.Three commonly used lineup procedures (i.e., simultaneous, sequential, and elimination-plus) were used, and the presence of the target also was manipulated.Overall, familiarity and lineup procedure impacted identification accuracy, such that in target-present lineups, witnesses were more likely to make a correct identification when they were more familiar (i.e., had direct interaction) with the perpetrator and the sequential procedure was used.Furthermore, in target-absent lineups, witnesses were more likely to make a correct rejection when they were more familiar (i.e., had direct interaction) with the perpetrator and the simultaneous or elimination-plus procedure was used.Taken together, these findings suggest that familiarity, in terms of having a direct interaction with a perpetrator before the commission of a crime, positively influences identification accuracy.Furthermore, these findings provide new, important information regarding the simultaneous-sequential debate and the utility of commonly used lineup procedures when the witness is familiar with the perpetrator.
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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 | 0.043 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".