Knowledge Pluralism in First Nations’ Salmon Management
Bibliographic record
Abstract
There is growing interest in the “integration” of knowledge and values held by Indigenous peoples with Western science into natural resource governance and management. However, poorly conducted integration efforts can risk harming Indigenous communities and reifying colonial legacies. In this regard, dichotomous conceptualizations of Indigenous and scientific knowledges are problematic. In this research, we focus on the role of indigenous and scientific knowledges in the management of coho salmon (Oncorhyncus kisutch) on the west coast of Vancouver Island, British Columbia (BC) in a governance context featuring contested authority among First Nations (Indigenous peoples) and the government of Canada. We discuss an example from a particular Indigenous community, Tla-o-qui-aht First Nations (TFN), that has worked with other management bodies to establish practices for the restoration, enhancement and harvest of cuẃit (coho). After outlining relevant Tla-o-qui-aht values, knowledges and decision-making processes, we consider the pluralistic approach to Indigenous and scientific knowledges in Tla-o-qui-aht management of cuẃit and show that pluralistic, co-constitutive, and multiplicative understandings of Indigenous and scientific ways of knowing may provide better grounding for addressing challenges in integration efforts. We also emphasize the importance of engagement with FN community liaisons and deferral to FN leadership to align management efforts with FN structures of knowledge production and governance, maintain ethical engagement, recognize Indigenous agency, and support effective conservation, and management efforts.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.047 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".