Practical steps toward integrating economic, social and institutional elements in fisheries policy and management
Bibliographic record
Abstract
Abstract While international agreements and legislation call for incorporation of four pillars of sustainability, the social (including cultural), economic and institutional aspects (the ‘human dimension’) have been relatively neglected to date. Three key impediments have been identified: a relative lack of explicit social, economic and institutional objectives; a general lack of process (frameworks, governance) for routine integration of all four pillars of sustainability; and a bias towards biological considerations. Practical integration requires a ‘systems’ approach with explicit consideration of strategic and operational aspects of management; multidisciplinary or transdisciplinary evaluations; practical objectives for the four pillars of sustainability; appropriate participation; and a governance system that is able to integrate these diverse considerations in management. We challenge all involved in fisheries to immediately take five practical steps toward integrating ecological, economic, social and institutional aspects: (1) Adopt the perspective of the fishery as a ‘system’ with interacting natural, human and management elements; (2) Be aware of both strategic and operational aspects of fisheries assessment and management; (3) Articulate overarching objectives that incorporate all four pillars of sustainability; (4) Encourage appropriate (and diverse) disciplinary participation in all aspects of research, evaluation and management; and (5) Encourage development of (or emulate) participatory governance.
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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 | 0.175 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.021 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".