Common but differentiated rights and responsibilities in tuna fisheries management
Bibliographic record
Abstract
Abstract The UN Law of the Sea Convention (LOSC) and one of the implementing agreements of the Convention—the UN Fish Stocks Agreement (UNFSA)—mandates all states to cooperate in the management of highly migratory and straddling fish stocks. In doing so, the UNFSA specifies that the special requirements of developing states need to be taken into account. To date, except in the Western and Central Pacific Fisheries Commission (WCPFC), there is no formal mechanism to identify these differential responsibilities in tuna regional fisheries management organizations (RFMOs). Although some conservation and management measures exempt small‐scale and artisanal fishing vessels, power imbalances within RFMOs tend to favour the interests of more developed and larger distant water fishing nations over those of small developing coastal states. To facilitate the implementation of differentiated responsibilities as mandated in UNFSA, in this study we develop a three‐step framework that could be applied in the case of new conservation and management measure proposals. The framework has also been tested based on two developing countries and compared with a developed state in the Indian Ocean Tuna Commission and the adopted resolutions in 2019. To facilitate better transparency and equitable decision‐making processes across RFMOs, this framework could be adapted based on member states' fisheries management objectives and target and non‐target species.
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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 | 0.031 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".