Assessing the Effectiveness of Monitoring Control and Surveillance of Illegal Fishing: The Case of West Africa
Bibliographic record
Abstract
This paper assesses illegal fishing in West Africa, one of the regions most affected by Illegal, Unreported and Unregulated fishing (IUU) in the world. The catch, the economic loss and the amount recovered through Monitoring, Control and Surveillance (MCS) are calculated based on a reconstruction method, and the information made available through national MCS units, between 2010 and 2016 in an effort to assess the effectiveness of surveillance efforts in the region. Results show considerable loss of revenues for Mauritania, Senegal, The Gambia, Guinea Bissau, Guinea and Sierra Leone, estimated at 2.3 billion USD annually, while a minimal amount of 13 million USD is recovered through MCS. In addition, this paper finds that countries touched by the Ebola crisis (Guinea and Sierra Leone) drive a tremendous increase in the loss generated by illegal fishing. However, further analysis shows that the overall severity of illegal fishing, as defined by a range of types investigated here, declines as the fines against the most severe forms of IUU fishing increase. Finally this study finds that Sierra Leone and The Gambia have the highest scoring MCS systems, and were the countries where the most offenders are caught and charged with the highest fines, while Senegal’s new legislations which improved MCS during 2015 does not appear to show on the scoring results. This study finds that illegal fishing amounts the equivalent of 65% of the legal reported catch from West Africa and poses serious concern for food security, and the economy in the region.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".