Emergency food aid and household food security during COVID‐19: Evidence from a field survey in Senegal
Bibliographic record
Abstract
Abstract The effectiveness of food aid in reducing household food insecurity in developing countries has been extensively examined in previous studies. This study explores this issue in the context of COVID‐19, using the example of emergency food aid provided by the Senegalese government. Field survey data were collected from 4500 recipients and non‐recipients, and the matching method was used to examine whether there was a significant difference between the two groups. Several dimensions of food insecurity were explored through five indicators: the food consumption score and the coping strategies index from the World Food Programme and three indicators of simple, moderate and severe food insecurity based on the Food Insecurity Experience Scale of the US Food and Agriculture Organization (FAO). The results show that government aid has a negative and significant impact on the diversity and nutritional value of beneficiary households' diets. Nevertheless, this programme prevented the use of extreme coping strategies. Furthermore, government aid has a positive impact on food security as measured by negative experiences related to food access. Ultimately, despite low nutritional intake, the programme had a positive effect on recipients’ food access compared with non‐beneficiaries. Therefore, for future interventions, the government should promote local and more nutritious products to sustainably improve food security.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".