The quality of fisheries governance assessed using a participatory, multi-criteria framework: A case study from Murcia, Spain
Bibliographic record
Abstract
Global policy initiatives, such as the UN Sustainable Development Goals, consider good governance a prerequisite for sustainable management of natural resources. Resource governance to address ecological and socioeconomic challenges, however, is complex and difficult to measure, as it involves both formal and informal structures and processes. To fill this need, a governance framework was developed that specifies governance quality criteria and indicators along multiple dimensions that were assessed by five stakeholder groups. The seven principles of good governance were adopted as criteria of governance quality and 29 governance indicators were developed to measure these criteria. Diverse stakeholders were asked to assess the importance and status of these criteria for fisheries in the Region of Murcia, Spain. To connect stakeholder perceptions of governance quality with possible policy interventions, a qualitative methodology, cognitive mapping, was used to assess the perceived linkages among indicators, as well as the perceived importance and status of each indicator. This was done to provide practical insights into how to improve governance quality, given its multi-dimensional and normative nature. The relationship between fisheries governance quality and fisheries sustainability also was explored. While applied to a specific case in Spain, this participatory, multi-criteria governance quality assessment framework can be adapted for any fishery.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".