Narco‐Fish: Global fisheries and drug trafficking
Bibliographic record
Abstract
Abstract This study analyses drug trafficking associated with fisheries around the globe. Records of vessel interdiction carried out between 2010 and 2017 suggest that the global trade of illicit drugs relies increasingly on fishing vessels. Fishery‐based trafficking is growing. A key obstacle to understanding the scope of this problem is the limited data on activities that are intentionally obscured, such as drug trafficking. Using a Fermi estimation technique for determining unknown values from limited data, we analyse 292 known cases of fishing boats engaged in drug shipment between 2010 and 2017. Results suggest that drug shipment sizes per vessel are becoming smaller over time, even as the total flow of drugs is increasing. Counter‐drug enforcement intensifies this effect, suggesting that drug trafficking networks adapt to interdiction efforts making use of smaller vessels to lower the risk of seizure. The use of fishing vessels in drug trans‐shipment has tripled over the past 8 years to about 15% of the global retail value of illicit drugs. Small‐scale fishers are at risk of turning to drug trade as an economic buffer against poverty, especially in contexts of mounting competition over declining fish stocks or strict marine conservation. At the same time, illicit capital flowing from the narcotics trade into fisheries may be driving over‐capitalization of fisheries and unsustainable resource use, ultimately to the detriment of resource‐dependent coastal communities and marine ecosystems. Future research is needed to better understand whether and how small‐scale fishermen turn to drug trade to counter livelihood risks of various kinds.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".