Food security and climate shocks in Senegal: Who and where are the most vulnerable households?
Bibliographic record
Abstract
In the Sahel of West Africa, food security is a top development priority. Climate shocks threaten communities that rely on a single rainy season to grow crops and raise livestock. We exploit repeat surveys collected by the World Food Programme to quantitatively assess the year-to-year dynamics of household food security. Our methodology singles out the impact of climate shock on food access. We combine three variables, namely the Food Consumption Score, the Food Expenditure Share and the Reduced Coping Strategies Index to explore the access dimension of food security. Cluster analysis on the three variables leads us to 1) classify into categories, and spatially locate less and more food secure households; and 2) discuss the response of each category of household to seasonality and variability in climate. First, we find that in a drought year, some rural households – with average food security status – that normally do not use coping strategies actually have to use them. Second, we notice that food expenditure share increases in all categories of households, except one. Based on the different ways in which categories of households respond to (climatic) shock we recommend the design of targeted and more efficient interventions. We focus on Senegal because of the unprecedented opportunity to access repeat surveys, including an unusual one, taken during a crisis year. However, our methodology and recommendations can inform interventions in other Sahelian 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".