Expert Testimony on Eyewitness Evidence: In Search of Common Sense
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
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.
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 | 0.001 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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