Lessons Lost In Sentencing: Welding Individualised Justice to Indigenous Justice
Bibliographic record
Abstract
Indigenous offenders are heavily over-represented in the Australian and Canadian criminal justice systems. In the case of R v Gladue, the Supreme Court of Canada held that sentencing judges are to recognise the adverse systemic and background factors that many Aboriginal Canadians face and consider all reasonable alternatives to imprisonment in light of this. In R v Ipeelee, the Court reiterated the need to fully acknowledge the oppressive environment faced by Aboriginal Canadians throughout their lives and the importance of sentencing courts applying appropriate sentencing options. In 2013, the High Court of Australia handed down its decision in Bugmy v The Queen. The Court affirmed that deprivation is a relevant consideration and worthy of mitigation in sentencing. However, the Court refused to accept that judicial notice should be taken of the systemic background of deprivation of many Indigenous offenders. The High Court also fell short of applying the Canadian principle that sentencing should promote restorative sentences for Indigenous offenders, given this oft-present deprivation and their over-representation in prison. In this article, we argue that Bugmy v The Queen represents a missed opportunity by the High Court to grapple with the complex interrelationship between individualised justice and Indigenous circumstances in the sentencing of 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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.025 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.013 |
| 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".