Oppositions and alliances between ICCAT contracting parties through an analysis of co-sponsorship of management recommendations
Bibliographic record
Abstract
We conducted a social network analysis (SNA) of the co-sponsorship of management measures at annual ICCAT Commission meetings from 2016 to 2022 with the aim of understanding the alliances between contracting parties. Our findings revealed that European Union, the USA and, to a lesser extent, Japan, Canada or the United Kingdom appear as key players in terms of cohesion and fragmentation centrality indicators, while the most central proposals in terms of management measures concern those on sharks (mainly focused on banning shark finning) and those on mitigating the effects of fishing on turtles. Based on the evolution over time of centrality measures of the SNA, the decrease in nestedness indicates that "specialist" CPCs (those who submit few proposals each year) are less and less associated with “generalist” CPCs (those who submit several management proposals each year). Although ICCAT's social network does not show a fragmented structure with small groups of CPCs isolated from each other, 2 co-sponsoring communities have nevertheless emerged: the COMHAFAT and OPESCA groups. both structured around a regional homophily. This paper also discusses the socio-economic and political factors external to ICCAT meetings that can influence partnerships in the management of large pelagic fishes in the Atlantic Ocean. These influential factors illustrate the complexity of asymmetric relationships between countries operating with long-distance fleets and developing coastal countries.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".