Indispensable sentencing tool or inconsistent sentencing technique?
Bibliographic record
Abstract
Two decades have passed since section 718.2(e) of the Criminal Code of Canada was enacted and subsequently interpreted by the Supreme Court of Canada in the landmark case R v. Gladue (1999, 1 SCR 688). This section requires judges to consider the unique systemic and background factors of an Aboriginal offender during the sentencing process to establish a proportionate sentence, thereby emphasizing restorative justice. Since the Supreme Court’s judgement in Gladue, a special form of pre-sentence report, known as a Gladue Report, has emerged to provide a tailored, comprehensive assessment of an Indigenous offender’s circumstances to assist sentencing judges in complying with their statutory obligations. Reviewing Gladue and subsequent jurisprudence, as well as numerous reports, the author argues that Gladue Reports are not being administered consistently across Canada, with many Aboriginal offenders not receiving the proper consideration into their unique circumstances, known as Gladue factors. This constitutes a pervasive systemic problem of unequal access to justice. Analyzing the current use of traditional pre-sentence reports and the various models to produce and deliver Gladue Reports across jurisdictions, this thesis maintains that Parliament of Canada should consider amending the Criminal Code and develop a national framework for Gladue Reports to be made available for all Indigenous offenders.
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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 | 0.035 | 0.140 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".