Integrating diverse objectives for sustainable fisheries in Canada
Bibliographic record
Abstract
An interdisciplinary team of academics and representatives of fishing fleets and government collaborated to study the emerging requirements for sustainability in Canada’s fisheries. Fisheries assessment and management has focused on biological productivity with insufficient consideration of social (including cultural), economic, and institutional (governance) aspects. Further, there has been little discussion or formal evaluation of the effectiveness of fisheries management. The team of over 50 people (i) identified a comprehensive set of management objectives for a sustainable fishery system based on Canadian policy statements, (ii) combined objectives into an operational framework with relevant performance indicators for use in management planning, and (iii) undertook case studies that investigated some social, economic, and governance aspects in greater detail. The resulting framework extends the suite of widely accepted ecological aspects (productivity and trophic structure, biodiversity, and habitat–ecosystem integrity) to include comparable economic (viability and prosperity, sustainable livelihoods, distribution of access and benefits, regional–community benefits), social (health and well-being, sustainable communities, ethical fisheries), and institutional (legal obligations, good governance structure, effective decision-making) aspects of sustainability. This work provides a practical framework for implementation of a comprehensive approach to sustainability in Canadian fisheries. The project also demonstrates the value of co-construction of collaborative research and co-production of knowledge that combines and builds on the strengths of academics, industry, and government.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".