International experience in the regulation of the aquaculture products market
Bibliographic record
Abstract
The article presents a comprehensive analysis of international experience in regulating the aquaculture product market and state support mechanisms, using the cases of Norway, Canada, Japan, and Chile to generalize best practices and regulatory instruments. The analytical framework proposed in this study is suggested for adaptation in the context of reforming the national aquaculture market governance system. It has been established that aquaculture is rapidly expanding worldwide as a priority sector for food security and export development. Through the analysis of theoretical foundations, it is demonstrated that public support has evolved from conventional growth financing to the stimulation of innovation-driven, export-oriented, and environmentally sustainable development. The global experience of aquaculture regulation confirms that effective public policy can significantly accelerate the growth of this sector. Each of the reviewed countries has followed its own path in shaping an optimal mix of regulatory tools and support mechanisms. The article highlights that adapting these approaches with consideration of local specificities can ensure effective development of the aquaculture sector in any country aiming to unlock its potential for economic growth and food security. For Ukraine, which is developing its own aquaculture and related markets, the experience of these countries is particularly relevant in several key areas: the development of a national-level strategy helps align the efforts of government, business, and academia toward common objectives such as increasing production volumes, enhancing export capacity, and addressing environmental challenges; institutional support for innovation acts as a driver of progress, since government programs, grants, and tax incentives for new technologies result in more efficient production processes; science-based environmental regulation is essential for the long-term stability of the sector, through the establishment of monitoring and control systems that prevent environmental degradation and disease outbreaks, thus protecting businesses from losses and fostering public acceptance of aquaculture. State-driven integration into global markets for aquaculture products further contributes to expanding market access and capturing higher added value in the industry. Keywords: regulation, market, aquaculture, instrument, mechanism, food security challenges, circular economy, sector, export, added value.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".