Imagining and enacting Métis informed anti-racist education: A local Prairie K to 12 flower beadwork framework
Bibliographic record
Abstract
This article is based on findings from an anti-racist Métis/Michif partnership between the Gabriel Dumont Institute’s Saskatchewan Urban Native Teacher Education Program (SUNTEP) and the University of Saskatchewan’s College of Education. The study took a local approach through a series of anti-racist knowledge exchanges amongst 16 urban and surrounding-area Prairie Métis teachers and teacher educators. Drawing from Métis teachers’ experiences, Métis studies, and critical race theory, the article disrupts Eurocentric conceptions of anti-racist education by centring Métis experiential knowledge. Through qualitative and Métis methods, data was collected from five workshops that revealed an initial Métis-informed anti-racist education (MIARE) K–12 flower beadwork framework. The framework demonstrates how specific experiences of K–12 racialization are transformed into anti-racism through Métis teachers’ intergenerational cultural knowledge protected by core Métis cultural values.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.048 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".