Moving Beyond Rights-Based Management: A Transparent Approach to Distributing the Conservation Burden and Benefit in Tuna Fisheries
Bibliographic record
Abstract
Abstract Determining the distribution of the conservation burden and benefit is a critical challenge to the conservation and management of trans-boundary fish stocks. Given current levels of overfishing and overcapacity in many trans-boundary fisheries, some or all participating States must necessarily reach a compromise with regard to their interests and carry some share of the conservation burden. This article proposes a new approach to distributing the conservation burden and benefit in trans-boundary fisheries, and explores this approach in the world’s largest tuna fishery: the tropical tuna fisheries of the western and central Pacific. Such an approach would enable Regional Fisheries Management Organizations (RFMOs) to transparently ensure that conservation burden and benefit distributions are consistent with international obligations. The article recommends that RFMOs consider developing decision-making frameworks that would enable existing scientific processes to determine the necessary extent of conservation measures, while a new conservation burden methodology would then determine the implementation of the measure and its impact on each member.
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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 | 0.146 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.029 | 0.030 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".