Food insecurity among households during the COVID-19 pandemic in Nigeria
Bibliographic record
Abstract
Following the recent worldwide crisis brought on by the prolonged COVID-19 pandemic and the ongoing war in Ukraine, food insecurity has emerged as the topic of conversation that is being addressed the most. This study's main goal is to disentangle the relationship between household socioeconomic indicators and other variables that may have an impact on food insecurity during the COVID-19 pandemic in Nigeria. Furthermore, Quantile regression was applied and the result shows that some socio-economic factors such as the rural and education level have a negative significant contribution to food insecurity while a household with accounts from financial institutions has a positive significant contribution to food insecurity in Nigeria. The Quantile regression results, however, also demonstrate that the percentage of working adults engaged in agriculture has a negative significant contribution to food insecurity, whereas the percentage of working adults engaged in wage work has a positive significant contribution to food insecurity, suggesting that the higher the percentage of working adults engaged in wage work, the less food insecurity there will be.Consequently, the government need to strengthen the importance of food security by investing holistically in agriculture as well as providing adequate security to farmers to attract more people to agriculture which in turn will contribute to higher food availability, fight poverty and hunger as well as combating the food insecurity among the household in Nigeria.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".