Community knowledge as a cornerstone for fisheries management
Bibliographic record
Abstract
The imperative to include stakeholders and rightsholders in fisheries management over the past 30 years has led to many changes in management regimes around the world, a key one being a move toward collaboration and co-management. This is reflected, for example, in Canada, where the newly revised Fisheries Act (2019, c.14, s.3) incorporates this imperative in part by citing “community knowledge” as a component in decision making for fisheries management. However, the lack of a formal definition makes it unclear what exactly is meant by “community” and when and how community knowledge can play a role in management. To investigate what community contributions to fisheries management can entail, and who these communities might include, we conducted a scoping literature review using the Scopus database to synthesize common outcomes from research on community involvement in fisheries management toward the goals of ecological, social, economic, and institutional sustainability. Enablers and barriers for successful collaborative initiatives were identified, covering conceptual, logistical, and communication-related factors. Key recommendations were compiled from a range of case studies to map a path toward full-spectrum sustainability for fisheries. From these principles and practices, we ultimately identified major considerations for the Canadian context, including the need to (1) clarify the distinction between fishing communities and the fishing industry; (2) strengthen social networks and communication channels to facilitate collective action; (3) track and transparently share successes and failures in collaborative efforts and outcomes; and (4) more explicitly consider community well-being as a fisheries management objective. From our synthesis, there are lessons to be learned for fisheries (social) scientists and managers working to enhance evidence-based fisheries management, whether within Canada or in other collaborative management settings globally.
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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 | 0.029 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.016 | 0.038 |
| Scholarly communication | 0.025 | 0.020 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".