Lineup and photo spread procedures: Issues concerning policy recommendations.
Bibliographic record
Abstract
This article examines the recommended changes to lineup reforms outlined by eyewitness researchers and the impact of these reforms on public policy as reflected in national guidelines for police identification procedures (Technical Working Group for Eyewitness Evidence, 1999). The limitations of these reforms are discussed. Alternative “best practices” for social science researchers, as well as for police, are proposed to minimize false-positive lineup selections and, consequently, convictions of innocent persons. The purpose of this article is to examine recent recommendations for improving outcomes of lineups and photo spreads in applied settings and to comment on some issues related to policy recommendations. The primary issue appears to be whether a best-practices 1 strategy should be used. An analysis of recommendations for changes in eyewitness identification procedures (Wells, 1988; Wells et al., 1998) and subsequently developed public policy (Technical Working Group for Eyewitness Evidence, 1999) are used to illustrate this issue. The relationship between eyewitness research by psychologists and the legal
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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 | 0.125 | 0.363 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.045 | 0.033 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".