Criminal Justice Theories and Variations in Legal Decisions Across Youth Justice Acts
Bibliographic record
Abstract
Media stories of violent young offenders, while rare events, “signal” to the public that youth crime is on the rise and worse, that today’s youth are capable of horrific crimes. As a result, both the public and politicians call for change – legislation and the courts need to toughen up on youths. The present study, guided by penal populism and focal concerns theory, fills a gap in the literature by examining sentencing decisions of young offenders convicted of violent offences in Ontario, Canada. Three research questions were asked. First, drawing on penal populism is there evidence in Canada, particularly Ontario, of a penal populist turn? Are judges taking a more punitive stance in sentencing young offenders convicted of sexual assault and physical assault? Second, drawing on focal concerns theory, do sentencing decisions reflect an offender’s level of blameworthiness and the need for protection of the public? Can extralegal factors, such as age and gender, explain disparities in sentencing? Third, do the sentencing rationales provided by judges vary within and across offence type? To answer these questions, a sample of sentencing decisions was analyzed to uncover quantitative and qualitative trends. The findings provide mixed support for penal populism and limited support for focal concerns theory. One prominent finding is that the courts appear to take a more punitive stance towards physical assault offenders regardless of their rehabilitative prospects than the sexual assault offenders. Three explanations are proposed for this difference. First, it appears that judges believe that physical assault offenders are better rehabilitated with a custodial sentence, while rehabilitation for sexual assault offenders is best achieved through non-custodial sentences. Second, a disproportionate number of physical assaults were committed against strangers whereas sexual assaults were disproportionately committed against people known to the offender; the courts may view the former offenders as a greater threat to the community. Lastly, there may be a lag between legislative changes and changes in sentencing patterns, as a result of judicial inertia. Although this sample is not representative, there is evidence of increased use of custody within these cases that become precedents for future sentencing decisions. As a result, judicial precedence may, in the future, result in harsher sentences for young offenders convicted of physical assault.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".