Bill C‐7: The new youth criminal justice act: A darker young offenders act?
Bibliographic record
Abstract
Recently, in Canada, there was a push to replace existing youth legislation, entitled the Young Offenders Act (YOA). This push was a result of several perceived failures of the YOA and has culminated in a new piece of youth legislation, Bill C‐7: The Youth Criminal Justice Act (YCJA). The Department of Justice has stressed that the YCJA will rectify the substantive problems of the YOA. This policy paper provides an analysis of the claims of the Canadian government by focusing on the substantive sections of the YCJA, comparing the YCJA with the YOA, and incorporating social science research dealing with the probable effects of the innovations. Specifically, this analysis focuses on the use of extrajudicial sanctions, adult transfers and the decreased emphasis on due process rights in the YCJA. The analysis shows that the YCJA potentially embodies a much more punitive model of youth justice in Canada, evidenced by its focus on the protection of society. Two of the major deficiencies of both acts are that they lack clarity in their respective Declarations of Principle due to a mixed model of justice and the insufficient use (probable in the case of the YCJA) of informal court measures. Lastly, this analysis uncovers that aside from the increased potential punitiveness of the YCJA, there is very little substantive difference between the YOA and YCJA. Thus, the YCJA is likely to suffer from the same deficiencies as the YOA.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| 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".