Investing in a Distinctions‐Based Approach: A Paradigmatic Shift for Métis Policy?
Bibliographic record
Abstract
Abstract Is Canada's federal policy paradigm related to Indigenous peoples shifting towards a distinctions‐based approach? This article considers the ways in which the idea of a distinctions‐based approach has taken hold in the political, policy, and institutional frameworks that govern the policy relationship between Canada and one of the three groups of constitutionally recognized Indigenous people: the Métis. By tracing the prevalence of this idea in federal policy discourse and assessing its manifestation in budget investments and policy developments, we show the promise of this paradigmatic shift for the Métis and consider some of its limitations in capturing the diversity of Indigenous lived realities. We ultimately conclude that these limitations coupled with the failure to embed the distinctions‐based approach in federal legislative and institutional structures and the enduring features of Canada's settler‐colonial system casts doubts on the promise of this new policy paradigm.
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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 | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.072 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| 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".