Food Insecurity During the COVID-19 Pandemic in Burkina Faso
Bibliographic record
Abstract
This paper investigates the implication of the COVID-19 pandemic on household food insecurity in Burkina Faso. We used data from the High-Frequency Phone Survey collected from the period June 2020 to June 2021 by the World Bank in collaboration with the National Institute of Statistics. To assess the persistence of food inadequacy, we estimated a dynamic linear probability model. Our results revealed that female and elderly household members were more likely to skip meals during the pandemic than their respective counterparts. For households that skipped a meal due to the pandemic, the likelihood of facing food insecurity in the subsequent month increased by 37 percent. Similarly, individuals who ran out of food in consecutive months were 0.28 times more likely to experience the same situation in the following month. While other shocks can cause food insecurity, the global health-related, economic, social, and information dimensions of COVID-19 created a distinctive and multifaceted form of food shortage that sets it apart from many other types of shock. These findings suggest the implementation of effective programs to respond to shocks and the mitigation effects experienced by most disadvantaged groups.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".