Logic and Research Versus Intuition and Past Practice as Guides to Gathering and Evaluating Eyewitness Evidence
Bibliographic record
Abstract
Psychologists have conducted extensive research and devoted substantial thought to the memory, cognition, decision-making, logic, and human interaction components of eyewitness evidence. It is fortunate that much of that work has been formally recognized by law enforcement and the legal community and used as the basis for procedure and policy changes with regard to how eyewitness evidence is collected and evaluated. The authors discuss reasons that some segments of law enforcement, the legal community, and the public resist these research findings (e.g., by seeing psychology's role as a way to discredit eyewitness evidence or being committed to established procedures that have no empirical support). The authors also address gaps between these common misconceptions and what the psychology research perspective has to offer, in an effort to gain even more support for research- and logic-based recommendations concerning eyewitness evidence.
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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 | 0.198 | 0.316 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.006 |
| Science and technology studies | 0.005 | 0.105 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".