COVID-19 and Urban Food Security in Ghana during the Third Wave
Bibliographic record
Abstract
While the effects of the COVID-19 pandemic on household food security have been documented, the intensity and forms of food insecurity in urban households in the Global South have not been adequately explored. This is despite the emerging consensus that impacts of the pandemic were more severe in urban than rural Africa. This paper addresses this knowledge gap by examining the relationship between pandemic precarity and food insecurity in Ghana’s urban areas during the COVID-19 pandemic in 2020. This study is based on the World Bank (WB) and Ghana Statistical Service (GSS) COVID-19 High-Frequency Phone Survey. Using a sub-sample of 1423 urban households, the paper evaluates household experiences of the pandemic. Our findings show that household demographic characteristics are not a major predictor of food insecurity. Economic factors, especially the impact of the pandemic on wage income and total household income, were far more important, with those most affected being most food insecure. Additionally, food-insecure households were most aware of and were affected by food-price increases during the pandemic. These findings are important in planning the post-pandemic recovery initiatives and in addressing current and future emergencies and shocks to urban food systems.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".