Gender Analysis of the Tuna Value Chain's Purchasing Stage in the South Central Provinces of Vietnam-Case Study of Binh Dinh Province
Bibliographic record
Abstract
This study aims to identify the gender disparities between men and women as purchasing actors in the tuna value chain in South Central provinces, thereby suggesting gender-sensitive policies towards gender equity, women’s empowerment, and sustainable tuna fisheries management. The study was conducted with the aid of a set of structured questionnaires executed through personal interview surveys. Tam Quan and Quy Nhon fishing ports in Binh Dinh province were chosen as study sites for conducting gender analysis at the tuna value chain's purchasing stage in the South Central provinces of Vietnam. Forty respondents were directly interviewed at their home or at the fishing ports in Binh Dinh province in May 2020, of which are ten middlemen and nine middlewomen purchasing yellowfin and bigeye tunas at Tam Quan fishing port; and twelve middlewomen and nine female traders purchasing skipjack tuna at Quy Nhon fishing port. This study used gender analysis tools such as the Harvard, Moser, and the USAID's six gender dimensions to identify gender differences between men and women in the tuna purchasing stage in productive, reproductive, and community managing roles. Some policy recommendations for improving gender equity, women's empowerment, and sustainable tuna fisheries management were proposed, including (i) describe the importance of women's reproductive role in gender-responsive strategies; (ii) establish tuna trading management board at the fishing port to collect information on the needs, issues, and interests of purchasing actors; (iii) organize training courses on tuna business and management skills at the fishing ports during the low season for the full participation of middle-actors, especially women; (iv) provide access to savings, credit, and microfinance for female traders to create opportunities for expanding their business; (v) improve facilities at the fishing ports to increase the overall well-being of the fish trading communities; (vi) introduce alternative job opportunities for laborers in the tuna purchasing stage during the low season; (vii) support laborers trading yellowfin and bigeye tuna in seeking other ways to make a living due to the depletion of these tuna resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".