The gender dimension of the agrarian transition: women, men and livelihood diversification in two peri-urban farming communities in the Philippines
Why this work is in the frame
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Bibliographic record
Abstract
Based on parallel field research conducted in two peri-urban villages in the cities of Naga and Valencia, the Philippines, this article deploys gender analysis to understand livelihood diversification in the context of agrarian change. In analyzing the role of state organizations and NGOs in (re)producing gender differences, hierarchies, roles and identities within agrarian settings, it brings poststructuralist and postcolonial theory into conversation with political economy to explore how gender is at stake in daily livelihood struggles. Specifically, attention is drawn to how structural constraints and institutional discourses still render livelihood diversification a gendered project, and how state and other development organizations are continuing to perpetuate gender inequalities and reinscribe normative gender discourses, particularly around masculinities and women's reproductive roles, in agrarian communities.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it