Stakeholder mapping to explore social and economic capital of Rotating Savings and Credit Associations (ROSCAs) to increase demand for and access to healthy food
Bibliographic record
Abstract
Introduction South Africa, grappling with the complexities of malnutrition, faces a dual challenge of undernutrition in children and overnutrition in adults, particularly among women. This situation is exacerbated by high rates of food insecurity, affecting nearly one-fourth of households. In this context, the role of Rotating Savings and Credit Associations (ROSCAs), locally known as stokvels, becomes increasingly significant. These informal, often women-led, savings and borrowing groups present a unique opportunity to address dietary challenges and promote healthier eating practices in urban, low-income settings. This study explores the potential of stokvels in mitigating the dual burden of malnutrition by facilitating access to healthy, affordable foods. Methods We conducted stakeholder mapping to understand the roles and influences of various actors within South Africa's food system, particularly their interactions with stokvels. Our research focuses on how these groups, deeply embedded in the community fabric, can leverage their collective power to negotiate better access to nutritious food and influence healthier dietary choices. Stakeholders identified in the study span diverse sectors, including retail, agriculture, finance, and community organizations. Results The research reveals that stokvels are perceived as vital social and economic entities capable of maximizing value through partnerships and networks. However, challenges such as the informal nature of stokvels and the lack of formal legal agreements often hinder their ability to form partnerships with formal institutions. The findings emphasize the importance of understanding and leveraging the social dynamics within stokvels, recognizing their role in enhancing food security and contributing to economic empowerment, especially for women. The study also identifies the need for formalizing stokvel structures to enhance their operational efficiency and increase their impact on food systems. Discussion In conclusion, this research highlights the untapped potential of stokvels in addressing South Africa's nutritional challenges. By fostering stronger connections between stokvels and various food system actors, there is a significant opportunity to improve food security and promote healthier eating habits in low-income communities. Future research should aim to include unrepresented stakeholders and explore strategies to enhance the role of ROSCAs in promoting healthier food choices and addressing affordability and accessibility barriers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 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".