Indigenous stream caretaking for Pacific salmon: ancestral lifeways to guide restoration, relationships, rights, and responsibilities
Bibliographic record
Abstract
Restoring the relationships, rights, and responsibilities of Indigenous Peoples to their salmon kin is central to a sustainable and just future with Pacific salmon, particularly as Nations lead the restoration of freshwater salmon habitat in their territories. As a group of Indigenous and non-Indigenous researchers from across British Columbia, we come together in a respectful and transparent way to uphold ancestral Indigenous Pacific salmon stream caretaking knowledge, longstanding Indigenous rights and relationships to land and waters, and our joint responsibilities to care for these watersheds. To do this, we begin by describing traditional governance systems that house Indigenous salmon stream caretaking practices. Through a literature review and conversations with co-authors, we then describe eight Indigenous salmon stream caretaking practices. Finally, we share three contemporary focal stories of Indigenous salmon restoration projects that uphold ancestral knowledge; “Syilx sockeye restoration”, “səlilwətaɬ (Tsleil-Waututh) led salmon habitat restoration in xʔə’l̓ilwətaʔɬ (Indian River Watershed)”, and nuučaanuɫ (Nuu-chah-nulth) Peoples and salmon: responsive methods through steadfast lifeways’. We present stream caretaking knowledge and the focal stories as learning opportunities that may guide future human-salmon relationships and restoration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".