The use of demeanour to assess the credibility of child victims in sexual interference trials
Bibliographic record
Abstract
Abstract How do judges assess witness credibility? This question is particularly important in cases where child victims testify. These cases are usually of a sensitive nature (e.g., involving allegations of abuse) where there are no other testimonial or material evidence to corroborate the account of child victims. To better understand what actually happens when judges preside over trials about sexual interference, and how, in actual courtrooms, they use demeanour to assess the credibility of child victims, we conducted a qualitative thematic analysis of real courts judgements (n = 44). The results highlighted that when assessing credibility in actual courtrooms, judges make a variety of inferences from the demeanour of child victims who testify live, with striking differences (and similarities) when defendants are found not guilty than when they are found guilty. The results of our descriptive study also provided unique insights about (correct and incorrect) beliefs judges hold about child witnesses, and how, if children fail to display behaviour they are expected to display, or if they display behaviour they are not expected to display, child victims could face difficulties at several stages of the judicial process. We discuss the results based on the literature on nonverbal behaviour and child witnesses, explaining their scope for scholars and legal practitioners.
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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 | 0.081 | 0.290 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".