The impact of Regional Fishery Management Organization structure on collaborative performance
Bibliographic record
Abstract
Collaboration is integral to understand, plan, coordinate, and implement management measures for fisheries that cross international borders. Regional Fishery Management Organizations (RFMOs) are the main arenas for countries to collaborate and make decisions for transboundary fisheries, but these organizations have generally failed to prevent the depletion of some of the world's most valuable fish stocks. It is unclear how the structure and functioning of RFMOs can improve collaborative performance to better manage transboundary fish stocks. Using fuzzy-set qualitative comparative analysis, our study analyzes 10 RFMOs to identify the combination of six organizational conditions associated with high and low collaborative performance. Using United Nations-mandated Performance Reviews conducted by internal and external experts we assess RFMOs for six collaborative performance metrics based on international standards. Our results show that high collaborative performance requires distinct roles for Secretariats and committees, meaningful representation of stakeholders, and a small decision-making body. Specifically, ‘a high number of committees’ combined with a ‘a high diversity of Secretariat duties’ led to low performance overall, but when combined with ‘a low diversity of Secretariat duties’ led to high conservation and management performance. ‘A small Commission size’ was a necessary condition for high overall performance and compliance performance. ‘Low stakeholder involvement’ led to low overall performance and low financial and administrative performance. Current trends in global governance call for RFMOs to increase their number of contracting parties and expand their mandates to address the growing environmental challenges affecting transboundary fisheries. Our results show that as the membership and scope of RFMOs expand, managers should prioritize the inclusion of stakeholders and technical experts over additional bureaucrats to achieve collaborative performance goals.
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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 | 0.022 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".