The discord between policy and practice: defence lawyersâ use of section 718.2 (e) and Gladue
Bibliographic record
Abstract
This study explores the differences (and similarities) between sentencing reform and the legal practices of criminal defence lawyers. This research specifically focuses on Section 718.2 (e) of the Criminal Code, which is aimed at reducing the use of imprisonment for Aboriginal offenders and the application of the section in the Supreme Court’s 1999 decision R .v. Gladue. It investigates whether or not the section and/or Gladue has affected the legal practices of criminal defence lawyers and if so, how. \n \nThe practice of lawyers, in this study, is conceptualized as structured action. The agency of lawyers is thus constrained and enabled by both macro and micro processes. These include traditional legal ideology, managerial/organizational ideology, presuppositions surrounding Aboriginality as well as the broader socio-political context of neo-liberalism and neo-conservativism. How the practices of defence lawyers either reflect or contradict the section and Gladue is examined through the oral narratives of lawyers—obtained through in-depth semi-structured interviews with twelve defence lawyers. \n \nThe findings of this analysis show that the vast majority of lawyers were not integrating the section or Gladue in their defence strategies. This suggests that efforts to remedy the issue of Aboriginal over-incarceration need to be aware of the complexity of criminal justice processes, the agency of lawyers and the broader social and political context.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".