The role of product diversification in enhancing market vendor adaptability and food-system resilience in Senegal, West Africa
Bibliographic record
Abstract
Severe food insecurity in Senegal, exacerbated by climate shocks and weak infrastructure, underscores the need to understand the role of market vendors in food system resilience. Unlike producers, vendors remain understudied despite their central role in food access. This mixed-methods study examines how product diversity, measured using the Shannon-Wiener index, influences Market Vendor Adaptive Capacity (MVAC) among 691 vendors in Sedhiou and Tambacounda. Survey and interview data reveal that diversity enhances MVAC, particularly for small retail and open-air vendors offering both staple foods and nutrient-rich products. Vendor characteristics such as employing staff, extending credit, and participating in training further strengthen adaptability, while systemic constraints like poor infrastructure and high transport costs limit benefits, especially in rural areas. Results indicate that diversity functions less as an independent driver and more as a strategic outcome of vendor capacity, reframing its role within resilience theory. The study contributes by (1) linking product diversity to adaptive capacity, (2) identifying enabling and constraining factors, and (3) outlining policy directions, including infrastructure investment, financial support, and vendor training. Strengthening these areas can expand food access, bolster resilience, and advance Sustainable Development Goal 2 (Zero Hunger) in Senegal with implication for West Africa.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".