Constructing Risk through Jurisdictional Talk: The Ontario Review Board Process under Part XX.1 of the Criminal Code
Bibliographic record
Abstract
Abstract The Ontario Review Board (ORB) makes and reviews dispositions that limit the freedoms of individuals found not criminally responsible (NCR) due to a “mental disorder.” Their dispositions must be responsive to the risk NCR individuals pose to the public. To assess how risk is measured, the authors studied twenty-six publicly accessible court files pertaining to the appeal of ORB dispositions. The authors studied hospital reports, the ORB’s dispositions, and transcripts of ORB hearings found in the court files. In this paper, the authors draw on institutional ethnography and critical legal theories of jurisdiction to analyze how certain citational practices—namely citation of closely related statutes and the ORB’s procedures—participate in structuring the ORB’s analysis of risk. The authors argue that risk becomes legible to participants in the NCR process through the intertextual mediation of these citations, which legitimize and naturalize the NCR individuals’ dependence on forensic institutions.
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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 | 0.119 | 0.292 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.034 | 0.031 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".