Subsidies and allocation: A legacy of distortion and intergenerational loss
Bibliographic record
Abstract
One of the greatest threats to the conservation of transboundary stocks is the failure of Regional Fisheries Management Organizations (RFMOs) to equitably allocate future fishing opportunities. Across RFMOs, catch history remains the principal criterion for catch allocations, despite being recognized as a critical barrier to governance stability. This paper examines if and how subsidies have driven catch histories, thereby perpetuating the legacy of unfair resource competition between distant water fishing nations (DWFNs) and coastal States, and how this affects ongoing allocation negotiations in the Indian Ocean Tuna Commission (IOTC). Using limited publicly available data on subsidies to Indian Ocean tuna fleets, we show that subsidies have inflated catch histories of many DWFN's. As long as historical catch remains the key allocation criterion, future fishing opportunities will continue to be skewed in favor of DWFNs, in turn marginalizing half of the IOTC member States, which collectively account for a paltry 4% of the current catch. Without better transparency in past subsidies data, accounting for this distortion will be difficult. We provide alternative allocation options for consideration, with our analysis showing that re-attributing DWFN catch to the coastal State in whose waters it was caught may begin to alleviate this historical injustice.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".