A Network Perspective on Spatially Clustered Territorial Use Rights for Fishers (TURFs) in Vietnam
Bibliographic record
Abstract
Co-managed territorial use rights for fishers (TURFs) have shown promise for small-scale fisheries management. The territorial use rights help clarify access and ownership rights, while co-management arrangements create formal relationships between fishers and government. However, there is limited research into the governance processes that influence the interactions and complementarities of TURF zones that are clustered together. In a network of 16 co-managed TURFs in the Cau Hai lagoon, Vietnam, we analyzed management decentralization and the relationship between spatial and networked (social) proximity. Our findings draw attention to several broad lessons for co-managed TURFs: (1) TURFs may operate as isolated silos if co-management agreements do not address relationships among TURF leaders; (2) spatial proximity does not automatically translate to social proximity; and (3) leaders of individuals TURFs need capacity for communication and coordination with other local fisheries leaders. These findings highlight the importance of consideration to the ways that TURF design and implementation influences the relationships and collaboration between fishers, government officials, and other actors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".