How to Make Individual Transferable Quotas Work Economically, Socially, and Environmentally
Bibliographic record
Abstract
Abstract The economic tool of individual transferable quotas (ITQs) gives their owners exclusive and transferable rights to catch a given portion of the total allowable catch (TAC) of a given fish stock. Authorities establish TACs and then divide them among individual fishers or firms in the form of individual catch quotas, usually a percentage of the TAC. ITQs are transferable through selling and buying in an open market. The main arguments by proponents of ITQs is that they eliminate the need to “race for the fish” and thus increase economic returns while eliminating overcapacity and overfishing. In general, fisheries’ management objectives consist of ecological (sustainable use of fish stocks), economic (no economic waste), and social (mainly the equitable distribution of fisheries benefits) issues. There is evidence to show that ITQs do indeed reduce economic waste and increase profits for those remaining in fisheries. However, they do not perform well in terms of sustainability or socially. A proposal that integrates ITQs in a comprehensive and effective ecosystem-based fisheries management system that is more likely to perform much better than ITQs with respect to ecological, economic, and social objectives is presented in this article.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".