Examining the effectiveness of youth diversion programming
Bibliographic record
Abstract
The ability to divert youth, found guilty of offences under the Canadian Youth Criminal Justice Act, away from formal sentencing sanctions is a fundamental principle and cornerstone of Youth Justice. This research paper contains both an analysis of the existing literature and the expert opinion of a Youth Diversion Program Coordinator in British Columbia (who will be referred to as Informant A). An examination of the existing literature indicated that youth diversion programs are effective in reducing recidivism rates among youth. This paper focuses specifically on the elements which contribute to a successful diversion program. These include: collaboration with the community and various stakeholders, mentoring, youth taking accountability and responsibility and police ‘buy in’ of the program. Interestingly, gender was found not to be a contributing factor to referral rates or successful completion of the diversion program. Various deficiencies in the literature are also discussed, including: challenges defining youth diversion, small sample sizes and lack of Canadian content. In summary, this research paper demonstrates that youth diversion programs are an effective measure in reducing recidivism rates among youth. These programs, when they contain the aforementioned elements above, are an acceptable means to hold youth accountable to the community.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".