Evaluating and implementing social–ecological systems: A comprehensive approach to sustainable fisheries
Bibliographic record
Abstract
Abstract Fisheries sustainability is recognized to have four pillars: ecological, economic, social (including cultural) and institutional (or governance). Although international agreements, and legislation in many jurisdictions, call for implementation of all four pillars of sustainability, the social, economic and institutional aspects (i.e., the “human dimensions”) have not been comprehensively and collectively addressed to date. This study describes a framework for comprehensive fisheries evaluation developed by the Canadian Fisheries Research Network ( CFRN ) that articulates the full spectrum of ecological, economic, social and institutional objectives required under international agreements, together with candidate performance indicators for sustainable fisheries. The CFRN framework is aimed at practical fisheries evaluation and management and has a relatively balanced distribution of elements across the four pillars of sustainability relative to 10 alternative management decision support tools and indicator scorecards, which are heavily focused on ecological and economic aspects. The CFRN framework has five immediate uses: (a) It can serve as a logic frame for defining management objectives; (b) it can be used to define alternate management options to achieve given objectives; (c) it can serve as a tool for comparing management scenarios/options in decision support frameworks; (d) it can be employed to create a report card for comprehensive fisheries management evaluation; and (e) it is a tool for practical implementation of an integrated social–ecological system approach.
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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 | 0.047 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".