The influence of eyewitness age, type of descriptor inconsistencies, and familiarity with defendant on mock jurors' perceptions of eyewitness testimony
Bibliographic record
Abstract
Eyewitness evidence can play a key role in juror decision making. This study examines the influence of eyewitness age (10 vs. 20 years old), type of descriptor inconsistencies (no descriptor inconsistencies, inconsistencies related to easy-to-change 'non-permanent' features or inconsistencies related to difficult-to-change, 'permanent' features), and familiarity with the defendant on participants acting as mock jurors' assessments of eyewitness and defendant integrity, continuous guilt ratings, and dichotomous verdicts. Participants were asked to read one of 12 versions of a trial transcript and then answered a self-report questionnaire. Eyewitness age did not have a significant effect on any dependent variables. Familiarity had a marginal effect on guilt assessments, both continuous and dichotomous. The presence of any descriptor inconsistencies led jurors to believe the eyewitness more, defendant less, and assign more guilt to the defendant. However, the type, i.e. non-permanent or permanent, did not differentially impact assessments. vii
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.
How this classification was reachedexpand
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".