Adaptation through knowledge coexistence: insights for environmental and sea lamprey stewardship
Bibliographic record
Abstract
Strategies for tackling environmental issues, including the consequences of invasive species and corresponding control efforts, are frequently approached through a Western scientific lens that often overlooks Indigenous rights and Indigenous knowledge systems. This can cause numerous issues from costly delays in implementing control programmes, overlooking vital ecosystem information and alternative options, legal action due to infringement on rights, and perpetuating systems of oppression. This research uses social science and Indigenous methodologies to understand the Denny’s Dam rehabilitation (DDR) as a case study for relationship-building and knowledge coexistence between the Saugeen Ojibway Nation and the Great Lakes Fisheries Commission in controlling sea lamprey (Petromyzon marinus), an invasive species in the Laurentian Great Lakes. To evaluate the successes and shortcomings of the project, virtual semi-structured interviews (n = 14) were conducted with key decision-makers and others involved in the rehabilitation of Denny’s Dam, a sea lamprey barrier. Analysis of these interviews identify four main factors that were crucial in the success of the DDR partnership: meaningful communication, funding and capacity, going beyond duty to consult requirements, and early engagement. The DDR shows how knowledge coexistence approaches, including Two-Eyed Seeing, can lead to equitable decision-making, foster collaboration, and contribute to addressing challenges like climate change, invasive species, and various environmental degradation.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| 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".