Effects of Mandatory Minimum Sentences on the Rights of the Indigenous Population in Canada: A Proposed Solution to Bill C-10's Conflict With Section 718.2(e) of the Canadian Criminal Code
Bibliographic record
Abstract
A number of Canadian laws underwent mass revision in 2010 with the passing of Bill C-10 – an expansive piece of legislation that amended a variety of laws, including the Canadian Criminal Code, lengthened sentences, and introduced a range of mandatory minimum sentences. Since its passing critics have noted the tension between Bill C-10’s mandatory minimums, and affirmative active legislation contained in Section 718.2(e) of the Canadian Criminal Code, requiring that judges consider the background and unique circumstances surrounding Indigenous offenders, and when appropriate, use discretion when sentencing. \n This thesis analyzes the feasibility of a safety valve for mitigating the conflict between Bill C-10 and Section 718.2(e) of the Canadian Criminal Code. In part, the thesis seeks to determine whether a safety valve option was considered during the framing of Bill C-10. The research focuses on the Canadian government’s role in the formation of the Canadian Residential School Program, and analyzes the long lasting impacts of the programs associated trauma in connection with 718.2(e) of the Canadian Criminal Code. Further, the study explores the conflict between Bill C-10’s mandatory minimums, and 718.2(e) of the Canadian Criminal Code’s judicial discretion requirement, ultimately suggesting that implementation of a safety valve may reduce the tension between the two pieces of legislation.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".