Two eyes, one vision: Evaluating the Etuaptmumk/Two-Eyed Seeing Framework in bridging scientific and indigenous knowledge for climate solutions.
Bibliographic record
Abstract
Canada’s colonial legacy has long marginalized Indigenous peoples and their knowledge in academia and policy-making. To bridge this gap, Mi’kmaw Elder Albert Marshall introduced the Etuaptmumk/Two-Eyed Seeing (E/TES) framework, aiming to optimize Indigenous knowledge (IK) and Western science. While E/TES is applied in research on fishery management and healthcare, its role in climate research remains underexplored in a cross-cultural context. Using a three-round Delphi study, this study identifies challenges in applying E/TES and strategies for fostering culturally respectful research environments. Findings reveal that common obstacles include the superficial inclusion of IK, the power imbalance between researchers, and di%culties reconciling opposing worldviews. To address these challenges, experts recommend abiding by the research protocols of Indigenous communities, establishing advisory bodies, and providing institutional support for Indigenous-led research. By scrutinizing the operational model of E/TES climate research, this study hopes to guide more inclusive, sustainable, and culturally appropriate research practices and environmental solutions.
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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 | 0.198 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.024 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.004 | 0.009 |
| 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".