Preventative Detention Decisions: Reliance on Expert Assessments and Evidence of Partisan Allegiance within the Canadian Context
Bibliographic record
Abstract
The purpose of this study was to examine judges' written reasons for sentencing in preventative detention hearings and the expert risk assessment reports presented, to determine the level of reliance placed on expert risk assessment reports and to examine the presence of partisan allegiance within the Canadian context. Results demonstrated that judges' decisions were consistent with expert assessments in terms of risk, treatment amenability, and risk management. Experts' ratings of treatment amenability and risk management were also significant predictors of the designation outcome, indicating that judges rely on this information in making their final decision. Finally, there was evidence of partisan allegiance, with prosecution-retained Psychopathy Checklist-Revised scores being significantly higher than defense-retained experts' scores. The results have implications for the development of consistent guidelines for the communication of risk, treatment amenability, and management information.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".