Determinants of household food resilience to Covid-19: Case of the Niayes zone in Senegal
Bibliographic record
Abstract
Abstract The Covid-19 crisis had negative economic and social effects worldwide, and its repercussions have been more significant on vulnerable populations. This article examines the food resilience capacity of households in the Senegal’s Niayes area during the first wave of Covid-19, regarding the quality and quantity of meals consumed. We use an ordered probit model with field survey data collected from 443 households. Results highlight some significant determinants of household food resilience, including public and private social protection measures in place before and after the crisis. The ARC-Replica NGO Consortium’s money transfer program has enabled households to improve their food situation for all three included periods. However, food aid from the Senegalese government and the United Nations has been ineffective. Furthermore, measures settled to support households during the hunger gap (aid from ARC-Replica and the Office of the Food Security Commissioner) improved households food resilience. Thus, results show that for aid to be more effective, its objectives and the implementation period must be in line with the expectations and needs of target population. Therefore, the aid settled for agricultural households is more relevant during the hunger gap. Finally, endogenous resilience strategies, including diversity of income sources and migrant remittances, did not improve household food resilience during Covid-19. This highlights the importance of the negative internal and external effects of the crisis on households, but also the need for effective and recurrent social protection measures to sustainably improve household resilience to shocks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".