Understanding Risk in the Context of the Youth Criminal Justice Act
Bibliographic record
Abstract
Increasingly, risk assessments are being adopted throughout the criminal justice system in Canada, and they are playing a significant role in the preparation of pre-sentence reports. Over the past five years, nine of Canada's jurisdictions have adopted the use of risk assessments for the preparation of youth pre-sentencing reports (PSR). While ample theoretical knowledge about risk exists, there are noticeably fewer empirical studies of how risk frameworks (re)shape and (re)organize local institutional decision-making practices or of what the socio-political effects of these processes are. Drawing on interviews, case law review, and analyses of risk-assessment documents, this article considers the implications of using risk assessments in the preparation of youth pre-sentencing reports (PSRs). In particular, attention is paid to the tensions between emerging risk practices and the emphasis on proportional sentencing under the Youth Criminal Justice Act (YCJA).
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".