Bibliographic record
Abstract
This article discusses the U.S. Coast Guard’s use of shiprider agreements to enforce maritime law. Shiprider agreements supplement current bi-lateral agreements between the U.S. and other countries by allowing U.S. Coast Guard (USCG) personnel and maritime law enforcement officers from other countries to extend legal authority by riding on the flag vessels of each partner country. This practice increases legal capacity and competence in combating such illegal maritime activities as fisheries violations and drug trafficking, while increasing awareness of maritime domains. Fisheries violations, involving shiprider agreements between the U.S. and such partner countries as the People’s Republic of China and the Republic of Cape Verde (Africa), are especially emphasized in this article. Comparisons are made to drug trafficking because illegal, unregulated, and unreported (IUU) fishing threaten living marine resources and ocean ecosystems worldwide. The use of national sovereignty as a deterrent to law enforcement is also discussed in detail, noting that criminals previously escaping law enforcement by crossing the national border of an adjacent country may now be pursued via shipriders. In an example involving the U.S. and Canada, a 2007 pilot project partnered the Royal Canadian Mounted Police (RCMP) and the USCG. The joint RCMP-USCG operation boarded 187 Canadian and U.S. vessels, seizing more than 200 pounds of marijuana, over one million contraband cigarettes, … and six vessels used in illegal smuggling operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.200 | 0.120 |
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".