The Impact of the Youth Criminal Justice Act on Case Flow in Alberta and System Response in Calgary
Bibliographic record
Abstract
The purpose of this study is to examine how the implementation of the YCJA has affected the flow of cases through the youth justice system, and the impact of the new legislation on workload for frontline staff in the youth justice system (i.e., police officers, probation officers). To achieve this goal, two main research activities were undertaken. First, CRILF examined the flow of cases processed through the youth justice system in Alberta from 2001 through 2006. This examination provides information on whether the youth system is adhering to the principles of diversion, fair sentencing and the reduction of incarceration mandated in the YCJA. Second, focus groups with police officers were conducted to identify the use of extrajudicial measures that would not be captured in the youth crime and correctional statistics and the effect of the new legislation on their work and workload. Focus groups were also conducted with probation officers to obtain information on changes in their workload patterns and their opinions on the success of rehabilitation and reintegration since the implementation of the YCJA. Participants of the focus groups were further asked to assess the current legislation's effectiveness in achieving its objectives and to provide suggestions for improvement to the current youth criminal justice system.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".