Envisioning Indigenous Community Courts to Realize Justice in Canada for First Nations
Bibliographic record
Abstract
Recognition continues to grow both within Canada, as well as the wider worldwide community, of the unique issues facing Indigenous people within Canada’s justice system. We see this in the recent wholesale adoption by the Canadian Government of the United Nations Declaration on the Rights of Indigenous Peoples, as well as the Truth and Reconciliation Commission Calls to Action. This article examines the potential development of a system of Indigenous community courts as a way to end colonial suppression of Indigenous self-governance. The article suggests as a model for these courts the tribal courts in the United States, as a means by which Indigenous peoples can re-instate Indigenous law and legal principles.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| 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".