Mandatory Minimum Sentences: Reforming Canada's Sentencing Practices
Bibliographic record
Abstract
In Canada, sentencing strategies have been actively debated and reformed since the early 1980’s. Sentencing policies are seen as mechanisms that can be used to deter crime and criminal actively. Canadian Parliament continues to implement legislative sentencing reforms that favour harsher, more punitive sentencing policies that are believed to deter offenders from committing crimes, such as mandatory minimum sentences. However, there is little evidence supporting these policies as effective measures that reduce offender recidivism and crime. Many similar jurisdictions that have previously implemented legislation supporting the use of mandatory minimum sentences have begun to amend and repeal legislation that supports their use, as research continues to expose what little benefits they have in comparison to the heavy fiscal and social costs that are incurred due to their use. In 2012, the Safe Streets and Communities Act, otherwise known as Bill C-10, made sweeping reforms to the Criminal Code. While Bill C-10 brought into force various changes to the criminal justice system, one of the most significant components of this bill were amendments made to the Controlled Drugs and Substances Act (CDSA), which increased pre-existing mandatory minimum sentences (MMS) and introduced new minimums for various drug-related offences. Amendments to the CDSA also broadened aggravating circumstances that can increase the length of a MMS for drug-related offences. Bill C-10 also amended s. 742.1 of the Criminal Code, restricting the use of conditional sentences of imprisonment through heightened eligibility restrictions. While conditional sentences of imprisonment (CSI) are still seen as a punitive sentencing measures, they also contain measures that focus on offender rehabilitation that have been proven to be more effective than incarceration. Due to these changes, federal and provincial governments will incur greater fiscal costs due to an increase in trial costs, correction costs and parole costs.1 These changes also induce greater social costs associated with the criminal justice systems increased reliance on MMS as incarceration is seen as a discourse that further impedes offender rehabilitation, which tends to propagate offender recidivism.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".