Bibliographic record
Abstract
In Canada, the practical application of youth diversion is rooted in an understanding of federal youth justice legislation and requires the consideration of police discretion. Yet, policing in Newfoundland and Labrador is shaped by localized practices, policies, and decisions. In the current article, we draw on online survey data to explore how Royal Newfoundland Constabulary (RNC) officers understand and apply Canada’s current federal youth legislation — the Youth Criminal Justice Act (YCJA) — and identify what factors, if any, influence the YCJA’s application. To unpack police officer attitudes towards youth and the YCJA and the actions police choose when handling matters involving youth, we draw from data collected from non-commissioned officers working in one of the three RNC detachments in 2016. Findings show that officers perceive a lack of YCJA resources available to front-line police officers in urban centres and a need for further training for officers who interact with youth. A desire for youth diversion services was evident among participants; however, the lack of availability of police-accessible pre-charge diversion options in Newfoundland and Labra-dor, including specific programs for youth, as well as police-specific training, are primary influencing factors affecting the understanding, implementation, and success of youth diversion in the province.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".