Re‐imagining the precautionary approach to make collaborative fisheries management inclusive of Indigenous Knowledge Systems
Bibliographic record
Abstract
Abstract Fisheries science uses quantitative methods to inform management decisions that reflect cultural preferences which, in turn, indirectly influence the states of ecosystems. To date, it has largely supported Eurocentric preferences for the commodification of marine organisms under the tenets of maximum sustainable yield, whereby abundances are intentionally maintained far below their historical baselines despite broader socio‐ecological trade‐offs. In contrast, Indigenous Knowledge Systems (IKS) adhere to the principle of “take only what you need and leave lots for the ecosystem,” implementing lower fishery removals to support socio‐ecological resilience. Despite the power imbalance favouring Eurocentric preferences in decision‐making, fisheries scientists increasingly recognize that the pairing of IKS and Western science, or Two‐Eyed Seeing, would lead to more holistic management goals. For recognition to transcend tokenism, meaningful collaborations and co‐governance structures underlying knowledge co‐production must carry through to legislated policy changes. Using recent co‐governance developments for fisheries management and spatial protections involving federal, provincial and Indigenous governments in Pacific Canada, we illustrate how the precautionary approach, including reference points and harvest control rules broadly applied in international fisheries, could be revised to make collaborative fisheries management compatible with IKS and improve biodiversity and fisheries protections. Our recommendations may create socio‐economic trade‐offs at different timescales for commercial fishers. Pre‐empting that challenge, we discuss IKS‐compatible economic approaches for addressing shorter term costs arising from reduced exploitation rates. Although our case study derives from Pacific Canada, the insights provided here are broadly applicable elsewhere in the world.
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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 | 0.030 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".