Building Collective Capabilities to Respond to Gender-based Constraints in Smallholder Farming: A Case from Rural Nepal
Bibliographic record
Abstract
Market uncertainties constitute a significant risk for smallholder farmers, particularly women. The COVID-19 crisis has underscored how entrenched gender norms are intensified during a period of crisis, thereby deepening existing gender-based constraints (GBCs) and limiting women’s opportunities in agri-businesses. Situated within the immediate aftermath of the post-COVID-19 context, this paper explores the lived experiences of women farmers in navigating GBCs and demonstrates how collective capabilities can challenge these barriers. Drawing on the approach and findings from a two-and-a-half-year participatory action research on women’s economic empowerment conducted in Nepal, we reveal that while social norms continue to dictate gender roles, traditional GBCs have evolved in response to infrastructural and technological developments, and they have manifested differently. However, enhanced capacities enabled women farmers to navigate niche market dynamics at higher nodes and secure premium prices for their produce. The approach to capabilities’ enhancement demands collaborative and context-suited actions, co-designed and co-implemented with women farmers and backed by related stakeholders. These findings highlight the transformative potential of building collective capabilities to address GBCs in smallholder farming by fostering women’s agency, enhancing their access to resources and strengthening institutional support for unlocking their economic potential in agri-business.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".