Aquaculture Governance Indicators: A diagnostic framework for steering towards sustainability
Bibliographic record
Abstract
The Aquaculture Governance Indicators (AGI) are an integrated social scientific framework for assessing governance performance for steering aquaculture sectors towards sustainability around the world. The AGI assess four governance dimensions against three governance principles. The four governance dimensions – legislation, voluntary codes and standards, collaborative arrangements and governance capabilities - allow for a systematic mapping of the governance landscape. The governance principles – legitimacy, effectuation, and coordination – focus on the organisation of roles and responsibilities, the implementation and effectiveness of enforcement, monitoring and learning, and the alignment of activities. This paper demonstrates the explorative and explanatory power of the AGI framework using the case of disease management in the salmon industries of Norway, Chile, and Canada. Our findings show that the governance of disease risk in these salmon industries is strongly supported by state legislation, yet remains limited in steering towards alternative solutions for avoiding or mitigating the effects of disease – and other persistent environmental challenges. We conclude that the AGI provides a valuable framework for self- or guided reflection and deliberation amongst decision-makers and stakeholders in aquaculture sectors around the world. Further development of the AGI framework will focus on a global set of country assessments, comparative analysis between production regions, comparison with other aquaculture indicator frameworks and the AGI’s potential for assessing the governance performance of food systems more broadly.
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.
How this classification was reachedexpand
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".