Bringing the salmon home: a study of cross-cultural collaboration in the Syilx Okanagan Territory of British Columbia
Bibliographic record
Abstract
In this study we explore the unique tripartite collaboration involving the Syilx Okanagan Nation Alliance and the provincial and federal governments, which has been pivotal in restoring Okanagan sockeye salmon <em>Oncorhynchus nerka</em> in British Columbia, Canada. Using qualitative research methods, we analyze the multidimensional challenges the tripartite partnership has faced over its 25-year history, emphasizing the role of traditional ecological knowledge, adaptive co-management, and social learning. We find that a conducive authorizing environment, shared goals, and an unexpected source of financing allowed the Okanagan Sockeye Program to launch. Once underway, the partnership relied on adaptive co-management strategies to navigate inherent complexities and uncertainties. Over time, the attainment of shared objectives and capacity bridging among the partners fostered trust and confidence, enhancing the sustainability of the alliance. Acknowledging the Okanagan Nation Alliance as a legitimate government and maintaining equal decision-making powers were also critical factors; however, Syilx traditional ecological knowledge, as a holistic knowledge-practice-belief system, has been the sustained driving force behind the partnership. Over the multi-decade trajectory, the alliance grappled with institutional complexities surrounding jurisdictional conflicts, power imbalances, and systemic inequities. At a time when Indigenous co-management in Canada is at a crossroads, and demands for Indigenous environmental governance are only increasing, this study offers some insights. Our research invites further exploration into the successes and failures of Indigenous environmental governance and co-management schemes, with a view to informing future policy and programming.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".