Normative Democratic Deliberation and the Role of Argumentation in the Canadian Mandatory Minimum Sentence Debate
Bibliographic record
Abstract
This paper explores the role of argumentation within the debates on Bill C-10, the Safe Streets and Communities Act, that came into force in 2012. Through examining Hansard transcripts, this paper aims to investigate how argumentation on mandatory minimums was utilized in this political decision making setting to legitimize and accomplish this policy initiative. I draw upon the concepts of normative democratic deliberation, new right ideology and the punitive turn to explore the Harper government’s use of argumentation strategies and discuss their implications for the Canadian political process and the current direction of the administration of justice in Canada. This paper’s goal is to contribute to literature on mandatory minimums and policy making through an exploration of the political deliberative process through which the C-10 provisions on mandatory minimums were adopted.
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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 | 0.046 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.045 | 0.053 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".