Mind the gap: Bridging evidence-based witness identification procedures to practice through police training
Bibliographic record
Abstract
Research has led to evidence-based eyewitness identification procedures, but empirical research on how to train police officers in such techniques is limited. We tested the effectiveness of the FAIR (Find a suspect, Avoiding bias, Instructing the witness, Record the procedure) eyewitness identification training program with 88 Brazilian police officers. The hypothesis that FAIR training would improve identification procedures was supported by pre- and post-training assessment of performance on a lineup construction task. Training increased the likelihood that police officers would construct lineups with known-innocent fillers and provide recommended pre-lineup instructions to witnesses (e.g., stating that the witness is not required to make an identification). Training also decreased non-recommended lineup practices, such as revealing the identity of the main suspect after a witness response. Feedback from the participants supports the conclusion that FAIR training improved knowledge of how to build lineups, instruct witnesses, and avoid undesirable and potentially biasing practices. Nevertheless, participants anticipated that incorporating the reform procedures into practice would provoke resistance from superiors and colleagues, which highlights the need for FAIR training to be supported by infrastructure, resources and policy that enable police officers to use evidence-based procedures in eyewitness identification.
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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 | 0.025 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".