Social Value Chain Analysis: The Case of Tuna Value Chain in Three South Central Provinces of Vietnam
Bibliographic record
Abstract
This paper presents the outputs of a Ministry of Science and Technology-funded national research project on fisheries value chain entitled “Developing Feasible and Comprehensive Policies for Sustainable Fisheries Development in Vietnam” completed in 2019. It was carried out to map the Vietnamese tuna value chain in terms of value chain description, including actors, material flows, volume, knowledge and information, relationships, linkages and trust, and values at different levels of the chain. The point of entry for undertaking this analysis was to identify specific income increasing interventions for fishers to achieve the project objective of better management of tuna fisheries and to improve socio-economic conditions of tuna fishing communities in Vietnam. Three South Central provinces of Binh Dinh, Phu Yen, Khanh Hoa were chosen for the investigation of the tuna value chain. This study was completed in four main phases, which consist of interview surveys, focus group discussions, individual key informant interviews, and a validation workshop. Four hundreds fishers, nineteen middlemen and traders, five processors, three wholesalers, and eight retailers were interviewed in the three investigated provinces during 2018. Several policy recommendations to increase the income and improve the position of fishers in the tuna value chain were proposed, which include (i) the collaboration among fishers to take advantage of purchasing input materials; (ii) the improvements on the handling and maintenance of tuna quality to increase fishers’ income; (iii) the establishment of tuna auction center to decrease financial detriment to fishers, increase their access to public and transparent market information, and strengthen their position in the chain; (iv) the formulation of savings, credit, and microfinance schemes to diversify forms of capital access for fishers; (v) the suggestion on a fair share of profits among shipowners, captains and cruise workers to reduce the vulnerability of the poor and increase the incentive for properly managing the tuna fisheries in Vietnam.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".