An Application of Two-Eyed Seeing: Indigenous Research Methods With Participatory Action Research
Bibliographic record
Abstract
In this time of reconciliation, Indigenous researchers-in-relation are sharing research paradigms and approaches that align with Indigenous worldviews. This article shares an interpretation of the Mi’kmaw concept of Two-Eyed Seeing as the synthesis of Indigenous methodology and participatory action research situated within an Indigenous paradigm of relevant, reciprocal, respectful, and responsible research. Two-Eyed Seeing is discussed as a guiding approach for researchers offering Indigenous voices and ways of knowing as a means to shift existing qualitative research paradigms. The author offers practical considerations for conducting research with Indigenous peoples in a “good and authentic way.” Through the co-creation of knowledge with Indigenous communities, a collective story was produced as a wellness teaching tool to foster the transfer of knowledge in a meaningful way.
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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 | 0.161 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".