Communities take the lead: exploring Indigenous health research practices through Two-Eyed Seeing & kinship
Bibliographic record
Abstract
Etuaptmumk or Two-Eyed Seeing (E/TES) is foundational in ensuring that Indigenous ways of knowing are respected, honoured, and acknowledged in health research practices with Indigenous Peoples of Canada. This paper will outline new knowledge gleaned from the Canadian Institute of Health Research and Chronic Pain Network funded Aboriginal Children’s Hurt & Healing (ACHH) Initiative that embraces E/TES for respectful research. We share the ACHH exemplar to show how Indigenous community partners take the lead to address their health priorities by integrating cultural values of kinship and interconnectedness as essential components to enhance the process of community-led research. E/TES is conceptualised into eight essential considerations to know in conducting Indigenous health research shared from a L’nuwey (Mi’kmaw) perspective. L’nu knowledge underscores the importance of working from an Indigenous perspective or specifically from a L’nuwey perspective. L’nuwey perspectives are a strength of E/TES. The ACHH Initiative grew from one community and evolved into collective community knowledge about pain perspectives and the process of understanding community-led practices, health perspectives, and research protocols that can only be understood through the Two-Eyed Seeing approach.
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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 | 0.034 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.025 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".