Mediating Métis Identity: An Interview with Jennifer Adese and Zoe Todd
Bibliographic record
Abstract
In this interview, Métis scholars and writers, Jennifer Adese and Zoe Todd, engage in conversation with settler scholar and PhD candidate, Shaun Stevenson, about the complex mediations of Métis identity. Pushing back against limited notions of “mixedness” and uncritical settler or non-Métis moves to indigenize at the expense of Métis peoplehood and self-determination, the authors articulate a robust mediation of Métis identity, which occurs through kinship relations, connections to a Métis homeland and polity, the potential for language revitalization, and the foregrounding of Métis women. The authors assert that Métis identity has been overdetermined through uncritical mediations of settler identity, as they shift the terms of conversation back to Métis peoples themselves, in order to articulate a distinct vision of Métis futurity.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.043 | 0.020 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.015 |
| 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".