Saugeen Ojibway Nation community input and action: Initiating a two-eyed seeing approach for dikameg (Coregonus clupeaformis) in Lake Huron
Bibliographic record
Abstract
Two-Eyed Seeing (Etuaptmumk in Mi’kmaw) involves seeing with the strengths of Indigenous ways of knowing through one eye, that of Western ways through the other, and using both eyes together to benefit all. Originating from Mi’kmaw Elder Dr. Albert Marshall, its benefits have been demonstrated across a broad array of disciplines, including fisheries research and management. We initiate a Two-Eyed Seeing approach to inform research priorities and explore solutions for the co-managed Saugeen Ojibway Nation lake whitefish (dikameg in Anishinaabemowin) fishery, in light of recent declines that remain poorly understood. We held interviews with harvesters and community members from the Chippewas of Nawash Unceded First Nation and the Chippewas of Saugeen First Nation through focus groups in each community, in addition to surveys that were completed at a community celebration in Nawash, to draw from the communities’ knowledge related to causes of dikameg declines, actions that could be taken to address them, and the future of the fishery in Lake Huron. Participants identified 19 themes, of which the most frequently mentioned were: 1) Harvesting, 2) Non-indigenous and invasive species, 3) Stocking of other fish species, 4) Habitat and water quality, 5) Assisting dikameg reproduction, 6) People and the community, 7) Ecological effects, and 8) Aquaculture and hatcheries for dikameg. Herein, we describe the communities’ concerns and demonstrate how these have formed the foundations of collaborative and community-based research initiatives aimed at applying Indigenous and Western scientific-based knowledge systems to better understand dikameg and their recent declines in Lake Huron.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".