Emergency food aid and household food security during COVID‐19: Evidence from a field survey in Senegal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 it