Women in Gendered Fisheries: Roles, Issues and Challenges in Cambodia, Indonesia, Vietnam and Philippines
Bibliographic record
Abstract
This paper is a synthesis of the results of the case studies on women’s situation in fisheries done by the members of the SEA Fish for Justice Network. The network is composed of 15 non-government and fishers organizations from the Southeast Asia region. It envisions equity in access to and control over off-shore, coastal and inland aquatic natural resources including the termination of suffering caused by unsustainable resources and/or privatized control over communal resources. The case studies were conducted by SEAFish Network members in Cambodia, Indonesia, Vietnam and Philippines in the second and third quarter of 2008 to highlight the roles, issues and challenges faced by women in coastal communities as well as the spaces provided them to facilitate their empowerment. The network members who conducted the studies were FACT (Cambodia), KIARA (Indonesia), MCD (Vietnam) and PROCESS-Bohol, CERD, and Tambuyog Development Center (CERD).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".