Evaluating and implementing social–ecological systems: A comprehensive approach to sustainable fisheries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it