“Two‐Eyed Seeing”: An Indigenous framework to transform fisheries research and management
Bibliographic record
Abstract
Abstract Increasingly, fisheries researchers and managers seek or are compelled to “bridge” Indigenous knowledge systems with Western scientific approaches to understanding and governing fisheries. Here, we move beyond the all‐too‐common narrative about integrating or incorporating (too often used as euphemisms for assimilating) other knowledge systems into Western science, instead of building an ethic of knowledge coexistence and complementarity in knowledge generation using Two‐Eyed Seeing as a guiding framework. Two‐Eyed Seeing ( Etuaptmumk in Mi’kmaw) embraces “learning to see from one eye with the strengths of Indigenous knowledges and ways of knowing, and from the other eye with the strengths of mainstream knowledges and ways of knowing, and to use both these eyes together, for the benefit of all,” as envisaged by Elder Dr. Albert Marshall. In this paper, we examine the notion of knowledge dichotomies and imperatives for knowledge coexistence and draw parallels between Two‐Eyed Seeing and other analogous Indigenous frameworks from around the world. It is set apart from other Indigenous frameworks in its explicit action imperative—central to Two‐Eyed Seeing is the notion that knowledge transforms the holder and that the holder bears a responsibility to act on that knowledge. We explore its operationalization through three Canadian aquatic and fisheries case‐studies that co‐develop questions, document and mobilize knowledge, and co‐produce insights and decisions. We argue that Two‐Eyed Seeing provides a pathway to a plural coexistence, where time‐tested Indigenous knowledge systems can be paired with, not subsumed by, Western scientific insights for an equitable and sustainable future.
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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 | 0.033 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.018 | 0.117 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".