An Application of Two-Eyed Seeing to Community-Engaged Research With Indigenous Mothers
Bibliographic record
Abstract
The Two-Eyed Seeing framework advocates viewing the world with one eye grounded in Indigenous knowledges while the other eye is grounded in Westernized knowledges. Research funding bodies have recently advocated for its use in research with Indigenous peoples, yet its interpretation and application in the literature has been inconsistent. To contribute to its maturation as a framework, this article describes the application of Two-Eyed Seeing to a community-engaged study aimed at understanding how Indigenous mothers experience using health care to meet the health needs of their infants in Hamilton, Ontario, Canada. Two-Eyed Seeing was applied to the research while applying the four R’s as suggested by Kirkness and Barnhardt’s: relevance, respect, responsibility, and reciprocity. While providing practical applications of this framework to research with Indigenous mothers and infants in an urban off-reserve setting, this article also contributes an approach to data analysis that incorporates Indigenous and Western knowledges within interpretive description methodology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.117 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".