Thirty years later: the global growth of ITQs and their influence on stock status in marine fisheries
Bibliographic record
Abstract
Abstract Individual transferable quota (ITQ) programmes have been incorporated into many marine fisheries management strategies for 30 years, but their implementation and utility remains controversial. This study provides an overview of the global status of ITQ programmes, the reasons they have been adopted and the changes in stock biomass after their implementation. Eighteen countries currently use ITQs to manage several hundred stocks of at least 249 species. ITQs were adopted in these countries for many reasons: overcapitalization, economic gains, safety concerns for fishers and political change. The implementation of ITQs does not translate into consistent changes in stock biomass. Improvements in 12 of 20 stocks after ITQs were introduced suggest that ITQs can be an effective component of fisheries management strategies, but eight of the stocks continued to decline after ITQs were introduced. This suggests that alternative or complementary measures are needed to sustain those fisheries, such as combining ITQs with more effective total allowable catches, better enforcement and monitoring, and implementing aspects of ecosystem‐based fisheries management.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".