Dynamics of aquaculture governance
Bibliographic record
Abstract
Abstract Aquaculture is a growing industry with an annual growth rate that is far superior to the population growth rate. Most production occurs in lower‐ and middle‐income countries, and therefore, they can improve the efficiency and modernize the production systems to increase exports to earn foreign exchange earnings for economic and social development. The institutional arrangements should be part of the mechanisms that ensure sustainable aquaculture growth, through the participation of all stakeholders. Sustainability is possible with good and dynamic governance through which the industry embraces the basic principles of governance, equity, accountability, efficiency, and predictability. Over the past decade, several countries made changes in governance and implemented regulations through their action plans to improve aquaculture productivity, and stakeholders profited from the changes made along the value chain. For the producers to benefit from the value‐added products, they complied with the regulations imposed by the importing countries, international regulatory bodies, or their own consumers. Standards increased, and the implementation of certification resulted in changes in the industrial structure. These standards, which inflict regulatory cost on producers, stimulated an improvement in productivity and product quality. However, during the last decade, production growth declined from 5.8% from 2001 to 2010 to 4.5% from 2011 to 2018, resulting in the elusive realization of the potential of meeting the sustainable development targets. There is a need for a paradigm shift that encourages small‐scale producers to engage in sustainable intensive aquaculture. The challenge is, therefore, to move toward production intensification and expansion, and the harmonization of national and international regulations to ensure the supply of safe and adequate fish to consumers, while maintaining a sustainable production system, and at the same time conserving the environment and maintaining social and economic stability. With good governance and political will, the social, economic, and environmental objectives for attaining the Sustainable Development Goals during the period 2020–2030 are possible if governments integrate sustainable aquaculture developments within an expanded aquatic and terrestrial food security policy framework using systems thinking and open innovation approaches.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".