Assessing Governability in Capture Fisheries, Aquaculture and Coastal Zones
Bibliographic record
Abstract
Capture fi sheries, aquaculture and coastal zones are closely-related resource systems with varying representations of diversity, complexity, dynamics and scale. They require different management approaches and appropriate governance structures which, as this paper suggests, can be determined partly through assessments of their governability. The governability of a resource system is defined as its overall capacity for governance, which is assessed by determining the properties, qualities and functionality aspects that make it more or less governable. The premise is that assessing governability might help to identify areas where governance can be improved. From an interactive governance perspective, we used a theoretical framework to assess qualitatively the governabilities of capture fisheries, aquaculture and coastal zones, focussing on the system-to-be-governed, the governing system, and the interactions between them. Overall, governability was found to be likely to be highest for aquaculture, moderate for capture fisheries and relatively low for coastal zones. One criterion that distinguishes aquaculture from the other resource systems examined is that it is generally owner-operated, making it more governable than the other systems. The results, strengths and weaknesses of the governability assessment framework used are discussed, with the aim of stimulating further development of methods and research on governabilities and governance of these and other resources systems.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".