Tackling IUU Fishing: Developing a Holistic Legal Response
Bibliographic record
Abstract
Abstract Illegal, unreported and unregulated (IUU) fishing is a global problem, which threatens marine ecosystems in addition to putting food security and regional stability at risk. It is often linked to major human rights violations and even organized crime. Legal measures, such as introducing monitoring and surveillance systems or denying services to vessels engaged in IUU fishing, are often implemented at national and international levels to combat such practices. Academics and economists have suggested that IUU fishing might be discouraged equally well by taking the profit out of it. Building on this premise, this article analyzes the extent to which the availability of liability insurance contributes to the problem of IUU fishing. To this end, an empirical study has been carried out, which supports the contention that vessels suspected of involvement in IUU fishing have no serious difficulty in obtaining liability insurance from the market and insurance sector, thereby inadvertently facilitating IUU fishing. The authors conclude that to deter IUU fishing, access to insurance for those involved in it should be restricted. Some success can be achieved if certain steps are taken to improve the risk assessment procedures of underwriters. However, it is advocated that the most effective approach would be the reform of European Union or domestic legislation and putting providers of liability insurance under a clear positive obligation to refuse cover to those involved in IUU fishing.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".