Adjacency and vessel domestication as enablers of fish crimes
Bibliographic record
Abstract
Fishery-related crimes, including illegal fishing, constitute major concerns including for coastal livelihoods and food security. This study examines the importance of adjacency, or legal presence within or in proximity to domestic fishing grounds and fish landing points, with regard to fishery crimes. Distinguishing between five main types of adjacency and examining cases from West Africa, the study finds that adjacency was a characteristic of a third of licensed vessels with reported fishery-related offenses in the region, 60% of which could be categorized as distant water fishing fleets. Fifty-four percent of the vessels authorized to fish in the region were foreign flagged, and 19% were foreign vessels reflagged to the coastal states, bringing up the contribution of foreign vessels to 73% of the fleets authorized to fish in the region. Vessel operators using a legal cover to commit infractions were mostly linked to China and Spain. This study points to the high likelihood of offense occurrence associated with the reflagging or “domestication” of foreign vessels, at least in West Africa, and the need to secure greater transparency and accountability in relation to access, offenses, and ownership.
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".