The impact of subsidies upon fisheries management and sustainability: the case of the North Atlantic
Bibliographic record
Abstract
Abstract This paper provides both an estimate and assessment of subsidies in fisheries in the North Atlantic. The subsidies are estimated, on the basis of data taken from an OECD study and the Sea Around US Project database, to be in the order of US$ 2.0–2.5 billion per year. The assessment of the impact of the subsidies upon resource management and sustainability requires an examination of the underlying economics of subsidies in fisheries. There is general agreement, to which we subscribe, that fisheries subsidies do great harm by exacerbating the problems arising from the ‘common pool’ aspects of capture fisheries. Many economists, however, believe it that, if the ‘common pool’ aspects of a fishery could be removed by, for example, establishing a full‐fledged property rights system, the negative impact of fisheries subsidies would prove to be trivial. This paper demonstrates that the aforementioned comfortable belief is unfounded. Fisheries subsidies can be seriously damaging, even if the ‘common pool’ aspects of the fishery are removed. There is also a widely held belief among economists and government officials that subsidies used for vessel decommissioning schemes, far from being harmful, actually have a beneficial impact upon resource management and sustainability, or are at worst, neutral. About 20% of the fisheries subsidies in the North Atlantic are directed towards these purposes. In this paper, we argue that these seemingly beneficial subsides can, in fact, be highly negative in their impact.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".