Fighting fire with fire: Why harsher punishments for young female offenders are not the answer
Bibliographic record
Abstract
Douglas College and the New Westminster Museum collaborated to host the Tick-Talk: Crime and Consequences Student Conference, which featured criminology students' presentations on a variety of crime, justice, and social issues. \nAdopting a fast-paced presentation format, students raised key issues and challenges, described personal experiences, and disseminated unique ideas in a public forum. Presentation topics included the right to legal representation, the over representation of Indigenous peoples in Canada’s criminal justice system, youth justice policy, and connections between mental health and criminal justice. The conference also included several discussion sessions that generated valuable dialogue among students, academics, practitioners, and members of the public.\n\n---\n\nCrime committed by young women has been increasing over the past several decades and researchers have few answers as to why. What is known about female offenders is that the vast majority of young women entering the criminal justice system have experienced sexual, physical and drug abuse, and mental illness. Rachelle Younie discussed the use of non-profit after-school programs, including their role in decreasing crime rates and their cost-effectiveness, as well as the harms of prison environments, including worsening mental health, increasing gang involvement and removing youth from prosocial connections. Criminal behaviour is a product of a number of sociological, psychological and economic disadvantages. Young women need positive resources to repair the underlying issues that led to their criminality, not to be punished for their upbringings.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".