Ethical Relationality and Indigenous Storywork Principles as Methodology: Addressing Settler-Colonial Divides in Inner-City Educational Research
Bibliographic record
Abstract
In this article, we share our engagement with Indigenous methodologies in a research study focused on teacher candidates in inner-city education. The study is conceptualized through ethical relationality as developed by Dwayne Donald (Papaschase Cree), and the principles of Indigenous Storywork as developed by Jo-ann Archibald (Stó:lō and St'at'imc). The study was enriched through encouraging a wholistic embodiment of ethics, revealing the presences of land and more-than-human teachers, and providing opportunities to transcend dualisms. We conclude with a consideration of the complexities, possibilities, and limitations of ourselves as Euro-descendant researchers, and the ethical requirements of Indigenous mentorship, time, and responsibility.
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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 | 0.060 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.085 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".