An Analysis of Public Support for Severity and Proportionality in the Sentencing of Youthful Offenders
Bibliographic record
Abstract
This study provides some data regarding public support for certain principles in the Youth Criminal Justice Act. It focuses on the concept of proportionality and explores the relationship between support for proportionality and support for more severe sentences. Data were obtained from 150 respondents to a questionnaire distributed in various neighbourhoods in Toronto during May and June 2002. The findings suggest strong support for proportionality, particularly amongst those who view sentencing under the Young Offenders Act as too lenient. However, although those favouring proportionality and those favouring harsher youth court sentences tend to overlap, support for each of these may develop in somewhat different ways. The findings suggest that few people would support or oppose all aspects of the YCJA. Thus if the government's goal was to try to give something to everyone, they may have succeeded.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".