Did the COVID-19 pandemic disrupt food security in West African rural communities? Survey results from four regions of Senegal and Burkina Faso
Bibliographic record
Abstract
Transboundary rural communities in West Africa play an important role in the exchange of goods, mainly food, among countries. The COVID-19 pandemic restricted these activities due to the closure of the borders. Semi-structured interviews were conducted in two regions of Senegal and Burkina Faso to examine the impacts of these restrictions on the pillars (availability, access, utilization, and stability) of food security in rural areas on men and women. The data set included 230 interviews, and they were analyzed through thematic content analysis. The results showed a decrease in agricultural production in all the communities due to mainly lack of labor force, and limited access to inputs, resulting in increased post-harvest losses. The disruption of trade and border and market closures affected rural families engaged in transboundary trade. Farmers experienced a sharp loss of household income leading to debts and decapitalization. Availability and diversity of and access to food was also heavily affected. Food security greatly varied among the communities and between countries. Perceptions also varied between men and women in terms of production, mobility, and food consumption. The restriction measures have triggered a spiral of effects and responses seriously impacting long-term food security in already highly vulnerable countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".