Influencing Cross-Race Eyewitness Identification Accuracy Using Photographic Lineup Procedures
Bibliographic record
Abstract
The elimination lineup was created to improve children's eyewitness identification accuracy, but recent research suggests that it may be suitable for use with adults. However, there is a lack of research on its robustness, particularly for cross-race identifications which are known to result in poor accuracy. There is also limited research investigating how lineup procedures affect cross-race identifications. The current study sought to explore how lineup procedures affect same- and other-race identifications, and investigate whether lineup procedures can moderate the cross-race effect. White participants watched a video of a White or Chinese male stealing money and were asked to identify the culprit in a target-present or -absent lineup, using one of three lineup procedures (simultaneous, sequential, and elimination).Results showed that lineup procedures varied in effectiveness depending on the presence of the target and whether a cross-race identification was being made. More research is required before denunciation of the simultaneous lineup.
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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 | 0.017 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".