The Economics of Overcapacity and the Management of Capture Fishery Resources: A Review
Bibliographic record
Abstract
This paper has its origins in a paper prepared by the author for a 1998 EU sponsored workshop, having the title: Overcapacity, Overcapitalization and Subsidies in European Fisheries (Munro, 1999). The author’s workshop paper drew heavily upon a FAO project with which he had been involved, on the management of fishing capacity, a project that was to give rise to the FAO International Plan of Action on Managing Fishing Capacity (FAO, 1999). Over the intervening decade, the issue of overcapacity in capture fisheries has been brought into sharper focus by calls for a massive resource investment program in the ‘‘natural’’ capital consisting of the world’s capture fishery resources. The widely cited World Bank/FAO report, The Sunken Billions (The World Bank, 2009), argues that, if capture fishery resources are to realize their full economic potential, they must be at least doubled in size. In order for this to happen, the report continues, there must be a 50% reduction in fishing effort, which would seem to call for a sharp reduction in fishing capacity (The World Bank, 2009, p. 42).2 The report has been accompanied by a complementary and currently ongoing OECD project on The Economics of Rebuilding Fisheries (OECD, 2010). With regards to fishing capacity, several broad questions will be seen to arise. To what extent, if any, is the overexploitation of capture fishery resources, necessitating the resource rebuilding program, due to the emergence of fishing overcapacity? What impact will the existence of the apparent fishing overcapacity have upon the nature of the proposed resource investment program Finally, what reliance should be placed upon fleet decommissioning / ‘buyback’ schemes to reduce fishing overcapacity and thereby advance the resource investment program? Properly speaking these, and similar questions, should be addressed, both within the context of ‘domestic’ (intra-EEZ) fisheries, and international fisheries, i.e., fisheries shared by two or more states (or entities). For reasons of length, if nothing else, the discussion will be confined largely to ‘domestic’fisheries.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".