Bibliographic record
Abstract
In this article, the author explores the criminal justice relationship between Canadian courts and Indigenous laws. More specifically, by examining two recent criminal law judgments from Nunavut that rely on Inuit customary law in their reasons, R v Itturiligaq and R v Ippak, the author queries and seeks to answer whether Canadian courts ought to engage with Indigenous laws, and if so, what appropriate engagement might look like. Through identifying both the promising and cautionary elements of these judgments, the author argues that Canadian courts should actively engage with Indigenous laws to achieve more responsive legal outcomes, but that great care and caution must accompany these efforts. The author proposes guidelines for engagement that involve establishing ethical space with Indigenous legal orders at the outset of judicial analysis to determine commensurability, followed by concurrent application where principles are mutually reinforcing or deference to Indigenous laws where principles conflict. The author concedes that judicial use of Indigenous laws is no panacea; rather, its use represents an important way in which the Canadian legal system can coexist alongside revitalized Indigenous legal systems and their institutions. The article is divided into four parts. In Part I, the author introduces the Nunavut court system and analyzes Itturiligaq and its application of Inuit Qaujimajatuqangit in light of the pluralist promise of R v Ipeelee, concluding that its reasoning instills both optimism and caution. Part II explores the philosophical and normative questions of whether Canadian and Indigenous laws are commensurate and whether non-Indigenous judges ought to engage with Indigenous laws in their judgments. In Part III, the author argues that Ippak best exemplifies judicial reasoning across legal orders that appropriately upholds both Indigenous and Canadian legal systems while respecting their incommensurabilities. Drawing on the lessons learned from Itturiligaq and Ippak, the author then develops his proposal for responsible engagement. Lastly, in Part IV, the author offers suggestions for institutional and legislative reform that must accompany judicial engagement with Indigenous laws. As the author indicates, the success of Indigenous-Canadian pluralism requires re-balancing sentencing objectives away from the rigid Canadian application of deterrence and denunciation and towards a system more focused on rehabilitation.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.029 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| 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".