The use of nonverbal communication when assessing witness credibility: a view from the bench
Bibliographic record
Abstract
The aim of this article is to provide a better understanding of how, in practice, judges use nonverbal communication during bench trials. The article starts with an overview of legal rules on how judges are supposed to assess witness credibility and use nonverbal communication, and briefly addresses the impact of those rules on lower courts and the limited data about judges in bench trials. Subsequently, we present the methods and the results from an online survey carried out with Quebec judges. While a number of judges have beliefs consistent with the scientific literature, findings reported in this article show that many judges have beliefs inconsistent with the scientific literature, and many are silent on culture-related differences in nonverbal behavior. The article ends with a discussion on the implications of the results for scholars and practitioners, including why findings reported in this article are cause for concern for adversarial justice systems.
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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 | 0.047 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".