Understanding Barriers in Indian Ocean Tuna Commission Allocation Negotiations on Fishing Opportunities
Bibliographic record
Abstract
Tuna Regional Fisheries Management Organizations (RFMOs) have been given an arduous mandate under the legal framework of the United Nations Fish Stocks Agreement. Member states with different interests and objectives are required to cooperate and collaborate on the conservation and management of tuna and tuna-like species, which includes the allocation of fishing opportunities. It is well understood that the main RFMO allocation disagreements are the inability to agree on a total allowable catch, the lack of willingness to accept new members, disagreement on who should bear the conservation burden, and non-compliance with national allocations owning to perceived inequities. Addressing these elements is crucial for any organization if it is to sustain its credibility stability and legitimacy. This paper identifies additional barriers facing an equitable allocation process at the Indian Ocean Tuna Commission (IOTC). These challenges are multi-faceted and include institutional, political, and scientific barriers in the ongoing allocation negotiations, and further inhibit effective negotiation and resolution adoption as a whole. After almost 10 years of negotiations, the process has progressed little, and without agreement on these barriers it will be a challenge to adopt a stable systematic allocation process.
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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 | 0.039 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".