Objection, your Honour: examining the questioning practices of Canadian judges
Bibliographic record
Abstract
Judges are the gatekeepers of evidence in the justice system. Granted that witness testimony is pivotal to the truth-seeking function of the criminal justice system, and that judges sometimes intervene and ask questions in the courtroom to help ensure the testimony is accurate, little is known about judges’ questioning practices. In the current study, we examine the questioning practices of a sample of Canadian judges. A total of 3,140 utterances spoken by 15 different judges across 22 criminal cases (169 witness examinations) were classified as one of 13 utterance types, and assessed as a function of examination type; utterance and response lengths were also calculated. Results showed that, when talking to witnesses directly, most of the questions asked were clarification (37%), followed by facilitators (17%), and closed yes/no (10%); less than 1% of all question types were open-ended. The longest answers were provided in response to open-ended questions. We also found that closed yes/no questions were the most frequently used question types during judge-led lines of questioning (i.e. examinations per curium), as opposed to lawyer-led lines of questioning (i.e. during direct and cross examinations). Implications for the truth-seeking function of the justice system are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".