Who Gets the Catch? How Conventional Catch Attribution Frameworks Undermine Equity in Transboundary Fisheries
Bibliographic record
Abstract
The focus on flag States for the purpose of attributing fisheries catch is inconsistent with the assignment of sovereign rights to coastal States under international law and undermines equity in contemporary quota allocation negotiations. We propose modernizing reporting frameworks to include zone-based reporting of fish catches to more equitably present data, ensure consistency with the Law of the Sea, and better support the realization by developing coastal States of their development aspirations consistent with SDG 14, Life Below Water. States are already required to collect the data necessary to support this change, and many regional fisheries management authorities already do so. Reforms to data collection and reporting mechanisms should support zone-based catch attribution as a central feature of negotiations around access to future fishing opportunities on shared resources. Doing so will ensure that the sovereign rights of developing coastal States are properly accounted for and implemented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".