Expert Evidence About Violence Risk Assessment: A Study of Canadian Legal Decisions
Bibliographic record
Abstract
In recent decades the practice of violence risk assessment and use of violence risk assessment instruments has become widespread in the criminal justice system. How are courts reacting to these developments? Herein the findings of a survey of Canadian case law are reported. Using Quick Law 35 cases were systematically identified in which judges commented on expert evidence regarding violence risk. Judicial comments were summarized with respect to evaluator qualifications, assessment procedures used, and presentation of findings and opinions. Findings indicate a wide variety of judicial preferences including the skill and knowledge of the evaluator, the type of information used, the description of findings and procedures, and the applicability of legal rules. Although potentially useful to evaluators caution is urged regarding the incorporation of some judicial preferences.
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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 | 0.022 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".