Empowering women through a gender-responsive seed multiplication program in Northern Malawi
Bibliographic record
Abstract
Limited access to quality seeds remains a major challenge to improving agricultural productivity and resilience to climate change in sub-Saharan Africa, especially in Malawi. Over the past decade, local seed multiplication programs have emerged as a promising solution to enhance farmers’ access to quality seeds in resource-poor regions. Some of such projects have incorporated gender-responsive tools to bridge the gender inequality gap in the seed system. Yet there is a dearth of research examining the impacts of seed multiplication programs on farmers’ livelihoods and in promoting gender equality in smallholder communities. This dearth of research has resulted in the underrepresentation of seed multiplication in agricultural policy. To address this void in the literature, we conducted interviews with 40 participants and three project officers from a gender-responsive seed multiplication initiative launched in 2011/12. Through a feminist political ecology lens, we investigated the program’s impact on gender equality and smallholder farmers’ livelihoods. Our results highlight prominent benefits farmers derived from the program; including livelihood diversification, seed and food security, improvements in women’s household decision-making autonomy, changing gender and social norms, and improvements in women’s leadership opportunities. However, challenges like climate change and constrained market access threaten these gains. Given these insights, we argue that gender-responsive seed initiatives can deliver on gender equality while simultaneously improving smallholder farmers livelihoods. While we advocate for the integration of gender-responsive seed multiplication initiatives into agricultural policy frameworks in Malawi, addressing market accessibility and promoting climate-resilient agricultural practices is crucial to realizing these gains.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".