Evaluating the Extrajudicial Measures and Sanctions within the Youth Criminal Justice Act
Bibliographic record
Abstract
Since the introduction of the Youth Criminal Justice Act (YCJA), there have been some concerns about the effectiveness of young people utilizing extrajudicial measures and extrajudicial sanctions. This article investigates if the implementation of these measures has created positive impacts for young offenders or if it is equivalent to just “a slap on the wrist.” Using a lens of restorative justice, the strengths and weaknesses of youth circles, youth committees, and victim-offender mediation programs are examined. This article explores the roles of those involved within extrajudicial measures and sanctions and addresses the gaps that exist within this section of the YCJA. Ultimately, this article finds that the restorative justice practices of extrajudicial measures and sanctions are effective at supporting young people throughout the legal process. It discovers that the programs offered for young offenders can give them a voice in their situation and create a connection to their community. However, there are changes needed in the areas of consistent data collection, proper fund allocation, and programming availability.
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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 | 0.071 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".