The Influence of Delay and Item Difficulty in Criminal Justice Systems on Eyewitness Confidence and Accuracy
Bibliographic record
Abstract
There is international concern about the negative effects of delays in Criminal Justice Systems.Problems include the deleterious effects that delay can have on witnesses' memory accuracy and witnesses' ability to calibrate their memories accurately.Little empirical work has been conducted on these issues combined with item difficulty and the relationship between accuracy and confidence.This paper investigates these issues.21 witnesses were interviewed about an observed crime and required to answer lawyerly questions used in crossexamination relating to target items classified as 'easy', 'moderate' and 'difficult', in terms of memorability.Participants were interviewed again, 6 months later.A 6 month delay significantly reduced memory accuracy for all levels of question difficulty.Within-subjects C-A relationships seemed to be relatively unaffected by delay; i.e. they tended to be positive for easy and moderate items, and negative for difficult items.Between-subjects C-A relationships were also positive for both easy and moderate items, but improved after 6 months; whereas C-A relationships for the difficult items remained negative and statistically insignificant following the 6 month delay.Delay can have a profound negative effect on witness accuracy that is not likely to be compensated for by improvements in C-A calibration.
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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 | 0.005 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".