Judicial Assessment of the Credibility of Child Witnesses
Bibliographic record
Abstract
This article reports on the results of two research studies carried out by the authors that address the questions of how and how well judges assess the honesty and reliability of children's testimony. One study tested the accuracy of judges and other professionals in assessing the honesty of children giving mock testimony. Judges performed at only slightly above chance levels, though the performance of judges was comparable to other justice system professionals, and significantly better than the performance of law students. The second study, a survey of Canadian judges about their perceptions of child witnesses, reveals that judges believe that compared to adults, children are generally more likely when testifying to make errors due to limitations of their memory or communication skills and due to the effects of suggestive questions. However, children are perceived to generally be more honest than adult witnesses. The survey also revealed that judges believe that children are often asked developmentally inappropriate questions in court, especially by defence counsel. There were no gender differences among the judges in either study. To put this research in context, the article first discusses the inherent challenges in assessing the credibility of witnesses and provides a review of the psychological literature and leading Canadian jurisprudence on the credibility and evidence of children.
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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 | 0.037 | 0.241 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".