Building trust through the Two-Eyed Seeing approach to joint fisheries research
Bibliographic record
Abstract
The Saugeen Ojibway Nation (SON) and the Ontario Ministry of Natural Resources and Forestry (MNRF) collaboratively govern the commercial fishery within the SON's traditional territory situated in Lake Huron’s main basin and Georgian Bay of the North American Great Lakes. Prior to the application of a Two-Eyed Seeing approach (Etuaptmumk), the two groups often operated in separate silos and relied on external experts to aid communication, which contributed to a sense of mistrust regarding the legitimacy of science that was being used to inform management. A breakthrough occurred when MNRF and SON began using the Two-Eyed Seeing approach to collaborate and jointly conduct research on fish populations in Lake Huron. In this article, we share how we used Two-Eyed Seeing to jointly develop a research proposal that is guided by both SON’s ecological knowledge and Western science. Our research involves addressing the role that lake trout (namegos; Salvelinus namaycush) have played in declines in lake whitefish (dikameg; Coregonus clupeaformis) abundance in Lake Huron, a priority identified by SON members. We share the challenges and lessons learned while reflecting on the ethical knowledge co-production framework we developed. Our goal is to provide a useful example of how Two-Eyed Seeing can be applied to foster relationship-building between government agencies and First Nations in the pivotal early stages of co-developing a research project. The Two-Eyed Seeing approach is foundational to building more equitable partnerships between Indigenous and non-Indigenous communities and for supporting healthy Great Lakes ecosystems and fisheries.
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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 | 0.160 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.021 | 0.056 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.007 | 0.010 |
| 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".