Constrained public benefits from global catch share fisheries
Bibliographic record
Abstract
Across publicly owned natural resources, the practice of recovering financial compensation, commonly known as resource rent, from extractive industries influences wealth distribution and general welfare of society. Catch shares are the primary approach adopted to diminish the economically wasteful race to fish by allocating shares of fish quotas-public assets-to selected fishing firms. It is perceived that resource rent is concentrated within catch share fisheries, but there has been no systematic comparison of rent-charging practices with other extractive industries. Here, we estimate the global prevalence of catch share fisheries and compare rent recovery mechanisms (RRM) in the fishing industry with other extractive industries. We show that while catch share fisheries harvest 17.4 million tons (19% of global fisheries landings), with a value of 17.7 billion USD (17% of global fisheries landed value), rent charges occurred in only 5 of 18 countries with shares of fish quotas primarily allocated free of charge. When compared with other extractive industries, fishing is the only industry that consistently lacks RRM. While recovering resource rent for harvesting well-governed fishery resources represents a source of revenue to coastal states, which could be sustained indefinitely, overcharging the industry might impact fish supply. Different RRM occurred in extractive industries, though generally, rent-based charges can help avoid affecting deployment of capital and labor to harvest fish since they depend on the profitability of the operations. Our study could be a starting point for coastal states to consider adapting policies to the enhanced economic condition of the fishing industry under catch shares.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".