Global Cooperation among Diverse Organizations to Reduce Illegal Fishing in the Southern Ocean
Bibliographic record
Abstract
Illegal, unreported, and unregulated (IUU) fishing is prevalent globally and has detrimental effects on commercial fish stocks and nontarget species. Effective monitoring and enforcement aimed at reducing the level of IUU fishing in extensive, remote ocean fisheries requires international collaboration. Changes in trade and vessel activities further complicate enforcement. We used a web-based survey of governmental and nongovernmental organizations engaged in reducing IUU fishing in the Southern Ocean to collect information on interorganizational collaborations. We used social-network analyses to examine the nature of collaborations among the identified 117 organizations engaged in reducing IUU fishing. International collaboration improved the ability to control and manage harvest of commercially important toothfish (Dissostichus spp.) stocks and reduced bycatch of albatrosses (Diomedeidae) and petrels (Procellariidae) in longlines of IUU fishing vessels. The diverse group of surveyed organizations cooperated frequently, thereby making a wide range of resources available for improved detection of suspected IUU vessels and trade flows, cooperation aimed at prosecuting suspected offenders or developing new policy measures. Our results suggest the importance of a central agency for coordination and for maintaining commonly agreed-upon protocols for communication that facilities collaboration. Despite their differences, the surveyed organizations have developed common perceptions about key problems associated with IUU fishing. This has likely contributed to a sustained willingness to invest in collaborations. Our results show that successful international environmental governance can be accomplished through interorganizational collaborations. Such cooperation requires trust, continuous funding, and incentives for actors to participate.
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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 | 0.004 | 0.007 |
| 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 | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".