Forging partnerships for women’s economic empowerment: A case of agro-cooperative-based commercialisation of legumes in Ramechhap, Nepal
Bibliographic record
Abstract
Agricultural cooperatives are internationally acclaimed as the “true agents of rural development”. Yet, their significance, prospects, and effectiveness in improving the rural economy in general, and women’s empowerment in particular, remain debated. In this paper, we explore a case of the journey of an agricultural cooperative into an agro-enterprise in Nepal, and analyse the potential, pitfalls, and pain points in such a transition and in empowering women. Our reflections are based on close engagement with the cooperative, women farmers, and private sector actors during a 30-month-long participatory action research project (PAR). Our findings indicate that Agricultural cooperatives can increase women’s income and promote their empowerment by providing opportunities to improve production, market skills, and knowledge, as well as facilitate inclusive decision-making and entrepreneurship capabilities. However, the path to cooperative-led agro-enterprise is fraught with challenges: procedural challenges, such as bureaucratic delays in business registration, obtaining food quality certification, and product brand registration; effectively navigating market dynamics, including maintaining quality standards, pricing, and market competition; and governance issues such as ensuring commitment to cooperative principles, equity, inclusion, transparency, and active participation of members. Notwithstanding these challenges, our study shows that co-operatives can contribute to commercialising agriculture and the economic empowerment of rural women. For successful transition, rural agricultural cooperatives, however, need government support and an enabling policy environment to promote their multi-functional nature. Additionally, it is essential to strengthen cooperatives’ internal governance, improve access to market information systems, and address financial, structural, and infrastructure barriers that impede interest and involvement in cooperatives.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Case report | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".