‘If we are tough on crime, if we punish crime, then people get the message’
Bibliographic record
Abstract
Through a detailed analysis of media reports and debates in the House of Commons over the late 1990s, this article explores the construction and proliferation of Canada’s punishable young offender. I suggest that the creation and dissemination of this discursive category resulted in calls for a new ethic of punishment that emphasized protection of the public from risks associated with youth crime. Media, political, and public concern about the punishable young offender propelled the Federal government’s announcement that it would replace existing youth justice legislation (the Young Offenders Act) with a tougher law premised on a framework of ‘accountability’. I begin by situating recent developments in Canadian youth justice policy domestically and internationally. Next, I highlight how the punishable young offender has been manifest in, and governed through, increasingly harsh penalties, austere punishments, and high rates of incarceration. Finally, I argue that calls for the punishment and intrusive regulation of juvenile deviance were pitted at two different, yet interrelated levels – the pervasiveness of the serious violent offender and valorizing victims of youth crime. In concert, these two levels prompted the Federal government’s denouncement of youth crime through tougher youth justice legislation (the Youth Criminal Justice Act).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".