A 20-year retrospective on the provision of fisheries subsidies in the European Union
Bibliographic record
Abstract
Abstract The next few months will be crucial in determining whether the world’s major fishing nations will deliver on commitments under the Sustainable Development Goals (SDGs) of the United Nations to prohibit harmful fisheries subsidies. Timing is of heightened importance given that the EU—the second-largest subsidizer—is reforming its financial instrument for fisheries. This article therefore examines the last 20 years of subsidies provided to the fisheries sector by the EU and supports discussion of the potential future for EU fisheries subsidies and the chance of success for the SDGs. Significant changes have occurred to EU fisheries subsidies during this period. Partly these changes have occurred as a result of the removal of certain capacity-enhancing subsidies and partly due to additional funds being allocated to beneficial forms of public funding. However, progress is slow and a significant amount of capacity-enhancing subsidies remain. Furthermore, the true extent of any reduction in capacity-enhancing subsidies may be shrouded by the Pollyannaish classifications of subsidization, but most disconcerting are the recent positions adopted by both the European Parliament and Council of the EU, which aim to reintroduce some of the most harmful subsidies, thereby putting the progress needed to achieve sustainable fisheries at risk.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".