After 30 years, what do we know about what jurors know? A meta-analytic review of lay knowledge regarding eyewitness factors.
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 typically characterize lay knowledge of eyewitness factors as low and highly variable. However, there are notable differences across methodologies, samples, and individual factors. To examine these differences systematically, we took a meta-analytic approach to reviewing the findings of 23 surveys assessing lay knowledge of eyewitness issues. Our analyses examined the beliefs of 4,669 respondents. Overall, respondents correctly agreed with survey items approximately two-thirds of the time. Results revealed significant differences in performance as a function of variable type, question format, and over time. We found few differences as a function of sample type, publication status, or jurisdiction. Although performance varied, a majority of lay respondents achieved "correct" consensus for as many as 11 of the 16 items included in this review.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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