Examining How Lineup Practices of Canadian and U.S. Police Officers Adhere to Their National Best Practice Recommendations
Bibliographic record
Abstract
Canadian (N = 117) and U.S. (N = 167) police officers completed a survey about their lineup construction and administration practices.We compared their responses to the respective national best-practice recommendations (BPRs) in place at that time; the two nations had five similar and four different recommendations.We predicted that if officers' lineup practices were to correspond with best-practice recommendations, officers' reports of their practices should be similar when national BPRs were similar, and differ in line with their country's BPRs when BPRs differed.We generally found the predicted pattern of results.Findings were especially striking when the BPRs differed.Some practices were largely in line with BPRs (e.g., double-blind testing), others corresponded to some extent (e.g., sequential lineups), and others were largely not followed (e.g., informing witnesses that it is as important to exonerate the innocent as it is to convict the guilty).However, even though our hypotheses were generally supported, there was considerable variation in practices that did not correspond with BPRs.We interpret these findings as demonstrating that BPRs have some influence on practices.Our findings illustrate the importance of assessing user reactions to BPRs and examining barriers to implementation of BPRs.The findings also indicate that BPRs can influence practice but demonstrate that, in the absence of the stronger action of setting legally binding policies, considerable departure from BPRs occurs.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".