The choices we make and the world they create: Métis conflicts with Treaty One peoples in <i>MMF v Canada</i>
Bibliographic record
Abstract
This article explores the intense fight over land between the Métis Nation and the Treaty One peoples (interveners) as it played out in the landmark Manitoba Métis Federation v Canada and Manitoba hearing at the Supreme Court of Canada. The article argues that this battle demonstrates the way zero-sum understandings of land, as well as racialized derivative title logics, animate the Treaty One peoples’ court strategy. By examining the contexts and complexities of these logics, the author shows that Treaty One peoples’ arguments undermine their own interests while also serving to undermine the formation of a coordinated Métis/Treaty One court strategy. The work uses Vine Deloria Jr’s interventions on time and the choices that make future worlds to argue that Métis and Treaty One peoples are not well served by narrowly self-interested litigation strategies. The piece concludes that shifting to a coordinated strategy would help avoid the weaknesses of Treaty One’s position while also better addressing the shared struggles of Métis and Treaty One peoples.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.091 | 0.062 |
| Scholarly communication | 0.024 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".