Expert Testimony on Eyewitness Evidence: In Search of Common Sense
Bibliographic record
Abstract
Surveys on knowledge of eyewitness issues typically indicate that legal professionals and jurors alike can be insensitive to factors that are detrimental to eyewitness accuracy. One aim of the current research was to assess the extent to which judges, an under-represented sample in the extant literature, are aware of factors that may undermine the accuracy and reliability of eyewitness evidence (Study 1). We also sought to assess the knowledge of a jury-eligible sample of the general public (drawn from the same population as the judges) and compared responses from a multiple choice survey with a scenario-based, response-generation survey in order to investigate whether questionnaire format alters the accuracy of responses provided (Study 2). Overall, judges demonstrated a reasonable level of knowledge regarding general eyewitness memory issues. Further, the jury-eligible general public respondents completing a multiple choice format survey produced more responses consistent with experts than did participants who were required to generate their own responses. The results are discussed in terms of the future training requirements for legal professionals and the ability of jurors to apply the knowledge they have to the legal context.
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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 | 0.064 | 0.405 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".