A policy agenda for pairing Indigenous knowledge systems and Western-based science to strengthen oceans and fisheries management in Canada
Bibliographic record
Abstract
It is increasingly recognized that the best way forward in fisheries management—for the health of aquatic ecosystems and fish stocks, for the rights and interests of Indigenous people, for non-Indigenous fish harvesters, and for Canada in general—is to develop governance arrangements and operational systems that build bridges and links between Indigenous Knowledge Systems and Western-based fisheries science. Success factors point to the importance of (1) working relationships founded around trust and continuity, (2) recognizing that Indigenous knowledges are place-based and unique to each culture, (3) safeguarding Indigenous data sovereignty and confidentiality, (4) supporting legal and policy arrangements, and (5) providing sufficient human and financial capacity. In the Canadian context however, several entrenched legal and policy obstacles remain to pairing Indigenous knowledges and Western-based science. Until such obstacles are eliminated, or at least substantially reduced, achievement of effective knowledge pairing and the multiple social, economic, and ecological benefits that flow from it will remain elusive in Canada. This paper offers a policy and legislative agenda for addressing these obstacles.
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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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.031 | 0.013 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 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".