Empowerment in agricultural value chains: Mixed methods evidence from the Philippines
Bibliographic record
Abstract
Women's participation and empowerment in value chains are goals of many development organizations, but there has been limited systematic, rigorous research to track these goals between and within value chains (VCs). We adapt the survey-based project-level Women's Empowerment in Agriculture Index (pro-WEAI) to measure women's and men's empowerment in the abaca, coconut, seaweed, and swine VCs in the Philippines and to investigate the correlates of empowerment. Results show that most women and men in all four VCs are disempowered, but unlike in many other countries, Filipino women in this sample are generally as empowered as men. Pro-WEAI results suggest that respect within the household and attitudes about gender-based violence (GBV) are the largest sources of disempowerment for both women and men, followed by control over use of income and autonomy in income-related decisions. Excessive workload and lack of group membership are other important sources of disempowerment, with some variation across VCs and nodes along VCs. Across all four VCs, access to community programs is associated with higher women's empowerment, and access to extension services and education are associated with higher men's empowerment. Our results show that, despite the relatively small gender gaps in the Philippines, persistent gender stereotypes influence men's and women's empowerment and VC participation.
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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 | 0.054 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".