Themes of Parole as Presented in Bill C-10: Contributing to the Conservative Government's 'Tough on Crime' Approach to the Criminal Justice System?
Bibliographic record
Abstract
Canada’s federal prison population has been rising for the past 10 years. This is perplexing given Canada’s national official crime rate has been declining since the 1970’s. One possible explanation for the rising prison population could be related to the restrictive measures imposed on parole policies during the last forty years. This thesis intends to analyze the recent parliamentary discourses surrounding recent legislative changes brought to parole by the conservative government. In doing so, a document analysis is conducted on the Parliamentary debates pertaining to section 6 and section 7 of Bill C-10 as well as the content of the amendments within section 6 and section 7 of Bill C-10. The purpose of the document analysis is to analyze the themes within these documents and determine whether or not these themes represent a potential change in the punitive approach towards parole. Given that a more punitive approach could have negative impacts on certain offenders and on society in general, this thesis aims to better understand the discourses and values of the Parliamentary debate participants’ changes to the legislation and the potential impacts these restrictions may have for Canada’s federal prison population.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.021 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".