Bibliographic record
Abstract
Responding to girls in the justice system has always remained a contested topic amongst Canadian policymakers. When the Youth Criminal Justice Act (YCJA) was first enacted, it was widely accepted due to its efficacy in reducing offending rates and promoting youth accountability (Bélisle, n.d.; Cesaroni & Peterson-Badali, 2017; Sprott & Doob, 2010). However, with the proliferation of research initiatives related to youth justice, there have been a number of flaws being accentuated pertaining to various facets of the justice system. Current programs and interventions designed for justice-involved youth fail to consider the multifaceted complexities and traumatic experiences contributing to girls’ system involvement. So, it is crucial to develop comprehensive strategies that: (a) help young girls effectively manage and cope with these risk factors to reduce their likelihood of offending, and (b) encourage the use of more appropriate and effective responses to girls who have committed a crime. The following policy reforms aim to specifically target and address female youth offenders’ risk factors: (a) the use of mental health programming/screening as both a preventative measure and a response to justice-involved girls, (b) implementing new interventions and restructuring previous ones to be more gender-responsive, and (c) learning from European policy reforms. My overall policy recommendation involves implementing more gender-responsive mental health and resilience programming for young girls.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".