Bibliographic record
Abstract
Abstract Sumaila, U. R. 2013. How to make progress in disciplining overfishing subsidies. ICES Journal of Marine Science, 70: 251–258. The World Trade Organization (WTO) has been working for more than seven years now to discipline overfishing subsidies, as mandated by the global community, without success. I argue that this failure is partly because WTO negotiators aim for an all-inclusive deal, i.e. negotiations are conducted as a “single undertaking”, whereby results must be achieved in all areas. Negotiators are required to broker an all-inclusive deal for all maritime WTO member countries and for all fisheries, whether domestic or international; small or large scale; developing or developed country fisheries. It is argued here that this commitment to a “single undertaking” does not align the incentive to remove subsidies with national interests, and therefore needs to be changed by splitting the world's fisheries into domestic and international fisheries. In this way, the battle for eliminating overfishing subsidies for some stocks would shift to home countries, and for others this would still rest with the international community. This split, it is argued, would align the incentives and improve the chances of eliminating overfishing subsidies.
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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 | 0.038 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".