Reflecting on the use of <i>Etuaptmumk</i> /Two-Eyed seeing in a study examining hospital-based Indigenous wellness services in the Northwest Territories, Canada
Bibliographic record
Abstract
/Two-Eyed Seeing (E/TES) is a Mi'kmaw guiding principle that emphasises the importance of bringing together the strengths of Indigenous knowledges and Western knowledges to improve the world for future generations. Since its introduction to the academic community, E/TES has been taken up more frequently in Indigenous health research. However, as it is increasingly used, Elders and scholars have affirmed that it is at risk of being watered down or tokenised. This article reports on how E/TES was used in a community-engaged research study that examined hospital-based Indigenous wellness services in the Northwest Territories, Canada. As a living, relational, and spiritual principle, E/TES was used in the study in three interrelated ways. E/TES: (1) guided the study ontologically, shaping the research team's conceptualisation of knowledge and knowledge generation; (2) informed the research team's approach to relationship-building; and (3) guided reflexivity amongst team members. By reporting on how E/TES was used in the study, and critically reflecting on the strengths and challenges of the approach, this article seeks to contribute to growing scholarship about how E/TES is characterised and taken up in Indigenous health research.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.019 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| 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".