Finding Your Allies Where You Can: How Canadian Courts Drive Aboriginal Policy in Canada
Bibliographic record
Abstract
While it has been valuable to Aboriginal peoples to have the courts as allies in their fight for state recognition, it is worth asking whether the slow, expensive, incremental process of achieving recognition through litigation is really the most efficient, let alone just, policy development process. Metis, Non-Status Indians, and Aboriginal women have all determined that litigation can be a useful strategy for achieving state recognition of their Aboriginality in the face of government intransigence. Yet the courts have proven to be imperfect, inconsistent, and not always reliable allies. This article reviews the cases in which Aboriginal women, Non-Status Indians, Metis, and urban Aboriginal people have sought to use litigation to drive the reform of rules for state recognition of Aboriginal peoples in Canada. These cases include not only successful litigation, but also occasions of which last resort to the courts has failed, revealing the difficulties and frustrations that Aboriginal peoples can face in having to rely on litigation to change government policy.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.066 | 0.021 |
| Scholarly communication | 0.022 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".