Gender Participation in the Capture and Marketing Sectors of the Glass Eel Fisheries in Aparri, Cagayan, Philippines
Bibliographic record
Abstract
The contribution of women in the fisheries sector is often underappreciated and under-valued because fisheries have long been considered a male-dominated sector. Studies have shown, however, that countries that value women’s involvement in this sector have reached high levels of economic growth and social well-being. Thus, the present study assessed the women’s participation in the capture and marketing sectors of the glass eel fisheries in the five coastal villages surrounding the mouth of Cagayan River in Aparri, Cagayan, Philippines. A household interview and a small group discussion were conducted to gather relevant and in-depth data on the different aspects like socioeconomic status, present roles, activities, responsibilities, access to and control over resources, and problems and constraints of both genders in the study area. Results have shown that out of the 146 respondents consisting of glass eel gatherers and consolidators, 95% are men and only 5% are women. The results showed that women have the ability to complement the family’s monthly income. However, they were limited by their reproductive roles and domestic responsibilities. Their engagement in the fishing sector is focused more on the pre- and post-harvest activities. Both sexes have equal access to productive resources, but men control the majority of them. Increasing the involvement of women in the formulation of policies and programs towards the management of available natural resources will lead to increased development and empowerment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".