Lineup and photo spread procedures: Issues concerning policy recommendations.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it