Ocean sustainability, anti-IUU policies, and the criminalization of small-scale fishers
Bibliographic record
Abstract
International concerns over maritime crimes have increased over the past two decades, with multinational efforts seeking to curb illegal fishing, piracy, drug trafficking, and slavery at sea. Many of these crimes have transnational and cross-sectoral dimensions contrasting with the limited regulatory reach and enforcement capacity of local authorities. The result is often a criminalization of small-scale fishing communities that fail to address more systemic and industrial-scale IUU. This paper seeks to bring conceptual clarity and systematic evidence on the development of anti-IUU legislation and enforcement practices in areas where small-scale fishing has been deemed to be associated with other criminal activities such as drug trafficking. Drawing from Latin American cases, the paper provides an account of the development of fisheries regulations, enforcement practices, and assessment of their impacts on small-scale fishing including associated patterns of criminalization of small-scale fishers. The paper then discusses policy implications to better understand the effects of anti-IUU strategies in aggravating or reducing harmful fishing practice and the criminalization of small-scale fishing communities, with the aim of contributing to foster greater sustainability and social justice within the fishing industry.
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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 | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".