Ensuring the Future of Métis Wellbeing through Digital Means and Heart-Work
Bibliographic record
Abstract
As Métis educators working in Alberta, we have long advocated for the meaningful inclusion of Métis perspectives and relational ways in educational spaces. We take up this work of advocacy and critical scholarship to honour our ancestors and ensure that the next generations of Métis are firmly rooted in their identity and empowered with confidence to assert their presence. Despite our efforts and those of many dedicated others who see the need for equity and inclusion in Indigenous education, our attempts to interrupt dominant settler-colonial logics have only been marginally successful. This article explores how a lack of knowingness around the Métis, flawed notions of racial purity, and assumptions of full assimilation of the Métis have resulted in ongoing injustices against the Métis. We share how we have worked to rectify these inequities through digital means alongside the prioritization of traditional Métis values that include respect, relational ways, and reciprocity.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.030 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".