David against Goliath? The rise of coastal states at the Indian Ocean Tuna commission
Bibliographic record
Abstract
Tuna regional fisheries management organizations (RFMOs) have long suffered from the domination of distant water fishing nations (DWFNs) in decision-making processes. The Indian Ocean Tuna Commission (IOTC) is no exception. In recent years, coastal states of the Indian Ocean (IO) have tried to change this dynamic - led by countries like the Maldives, Kenya, South Africa, and Australia - to deliver greater benefits to the region, including East Africa. These countries are gathered under the informal group of G16 and have increasingly improved their involvement in the IOTC. Here, we ask how the rise of the G16 benefited coastal States through participation and collective understanding in the Indian Ocean. To do this, we analyzed proposals submitted by the G16 for conservation and management measures and the participant lists of the Commission meetings in the past ten years. Our results show that, individually and collectively, the G16 has played a significant role in shaping the IOTC’s rules. The coastal States have established a good representation, with only a handful of Members absent in some years. Unveiling the efforts of coastal countries is essential to guide further capacity building in the region in terms of negotiations. We also call for international oversight of the actions of DWFNs, such as the EU, whose efforts often differ markedly from their claims of being sustainability champions. The G16’s work is essential to keep the coastal States of the Indian Ocean in the driver’s seat for managing Indian Ocean fisheries to benefit future generations.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".