Toward a more informative psychological science of eyewitness evidence
Bibliographic record
Abstract
Abstract Like Hugo Münsterberg, we believe that psychological science can inform the courts and police regarding eyewitness evidence. But 100 years into the enterprise, the body of knowledge acquired to date demands considerable circumspection, both in the claims expert psychological witnesses make in court and in the recommendations psychologists tender to investigating officers. There are a number of points regarding eyewitness evidence that psychologists can offer with considerable confidence, but many matters are as yet open to debate (and some issues are likely to remain unsettled for a long time). We encourage researchers, law enforcement and the legal community to (a) identify and prioritize the problems to be solved, (b) focus on a more integrative empirical approach, including more of the field experiments currently in use, as well as new descriptive research, especially on base rates and (c) use basic psychological theory and principles to consolidate the wide range of phenomena present in individual cases. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.093 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.019 | 0.005 |
| Science and technology studies | 0.004 | 0.052 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 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".