Gender analysis of the Orange Fleshed Sweet Potato Value Chain in Malawi: A systematic review
Bibliographic record
Abstract
<ns3:p>Background Sweet potato (Ipomoea batatas) is a key staple and commercial crop in Malawi that plays a critical role in food security, nutrition, and rural livelihoods. Despite women’s extensive contribution to production, processing, and marketing, gender disparities persist across the value chain. Objective This systematic review investigated the gender roles and barriers that limit equitable participation and benefit-sharing within Malawi’s sweet potato sector. Methods Using the PRISMA framework, we analyzed peer-reviewed literature published between 2015 and 2025, focusing on empirical studies addressing gendered access to land, credit, technology, markets, and decision making. Studies were evaluated using the Newcastle-Ottawa Scale to ensure methodological rigor. Results The results show that while women are central to the labor-intensive stages, they face systemic constraints rooted in sociocultural norms and institutional biases. Men disproportionately control land and income, dominate high-value markets, and prioritize extension and financial services. Existing policies, such as Malawi’s National Agriculture Policy and Gender Equality Act, advocate for inclusivity, but suffer from weak implementation, limited funding, and low institutional capacity for gender mainstreaming. This review highlights the urgent need for gender-responsive interventions, including reforms in land tenure, financial products tailored for women, inclusive agricultural technologies, and support for women-led enterprises and producer groups. Conclusion Integrating women’s voices into policy processes and strengthening multisectoral coordination is vital. Addressing gender inequities is not only a matter of social justice but also essential for unlocking the full development potential of the sweet potato value chain. The findings provide a foundation for targeted evidence-based policy and programmatic responses to advance gender equity and agricultural transformation in Malawi.</ns3:p>
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".