Less Law, More Justice: Rethinking Sentencing in the <i>Youth Criminal Justice Act</i>
Bibliographic record
Abstract
Abstract When it was first introduced, the Youth Criminal Justice Act (YCJA) had two primary goals of reducing the reliance on custody and increasing uniformity in sentencing practices. Twenty years later, the YCJA has succeeded in dramatically lowering overall rates of youth in custody, but this gain has been selectively experienced by non-Indigenous youth and regional disparities in sentencing practices persist. In this paper, we suggest that the YCJA’s inability to meet its goals is due to overcriminalization by over depth. Using Indigenous youth sentencing as a case study, we argue the YCJA’s layered and sometimes conflicting principles have symptoms of overcriminalization by over depth, including over- and under-inclusiveness, arbitrariness, and confusion in implementation. To more effectively meet the YCJA’s initial goals, we propose legislative streamlining and systemic reforms, including specialized Indigenous youth courts and enhanced community-based resources, as pathways to greater justice.
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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 | 0.024 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".