Sustainable Development of Small-Scale Fisheries and the Need for Strong Measures to Protect Small-Scale Fisheries in International Trade Law
Bibliographic record
Abstract
The small-scale fisheries sector makes a valuable contribution to livelihoods and food security, particularly in under-resourced countries. Yet small-scale fishers and fishing communities are often vulnerable and marginalised, and the small-scale sector is largely ignored by governments providing subsidies to their fishing industries. Provisions seeking to ban harmful fisheries subsidies are now the subject of several large international trade agreements and negotiations. While this is a laudable and necessary goal, the binding nature and robust enforcement mechanisms of trade agreements make it imperative that small-scale fisheries are protected and provided for in these agreements in the interests of sustainable development and poverty reduction. The thesis considers how this can be achieved. In order to determine what would best serve the interests of small-scale fisheries in trade agreements, the thesis creates a framework of development needs, which underpins the analysis in the remainder of the thesis. This analysis revolves around three large trade agreements and negotiations containing provisions on fisheries subsidies – namely the World Trade Organization (WTO) negotiations, the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP), and the United States-Mexico-Canada Agreement (USMCA). Drawing on the development framework, the thesis identifies a number of shortcomings in these agreements when it comes to protections for small-scale fisheries, including a lack of provision for important development needs and a failure to achieve an appropriate balance between development and sustainability considerations. The thesis also considers potential problems that could arise in the conclusion and enforcement of trade agreements dealing with fisheries subsidies, particularly as these relate to small-scale fisheries and sustainable development. Based on this analysis, the thesis makes a number of recommendations to be incorporated in trade agreements going forward that would adequately protect and promote the interests of small-scale fisheries, while not losing sight of sustainability concerns and the practical realities of negotiating complex international trade agreements. These include, inter alia, exemptions for important social assistance subsidies, better representation and transparency, and measures to improve equity between the small-scale sector and other fishing sectors.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".