Foregrounding and embodying Indigenous research methods and epistemologies in occupational science: Critical reflections from a Mi’kmaw researcher
Bibliographic record
Abstract
The persistence, strengths, and endless advocacy and activism from Indigenous Peoples and communities globally is shifting the research landscape. To actively challenge extractive and harmful research that has traditionally been done on Indigenous Peoples, by non-Indigenous Peoples, guidelines and calls for research that is done by, with, and for Indigenous Peoples have emerged. Within occupational science and therapy, there is a need for deeper engagement with the growing interdisciplinary literature marking out directions for research transformation. This manuscript shares the critical reflections of a Mi’kmaw researcher and occupational therapist who engaged in an Indigenous community-driven project with Indigenous occupational therapists. This project utilized individual storytelling sessions and a sharing circle gathering and culminated in initiating the formulation of an Indigenous Collective. In addition to describing the methodology, methods, and sharing how these align with Indigenous epistemologies, critical reflections on enacting this project within the context of completing a doctoral dissertation in a Canadian university provide insights into the centrality of foregrounding relationality, collaboration and Indigenous methodologies, and point to tensions in enacting these. By valuing and drawing from multiple perspectives (Etuaptmumk/Two Eyed Seeing), occupational scientists can meaningfully move to enact change and support the rights and sovereignty of Indigenous Peoples.
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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 | 0.083 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.051 | 0.098 |
| Scholarly communication | 0.026 | 0.017 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.009 | 0.026 |
| 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".