Immediate impacts of COVID-19 measures on bean production, distribution, and food security in Eastern Africa
Bibliographic record
Abstract
The outbreak of coronavirus was expected to adversely affect African countries more than any other region in the world. This assertion was based on the existing conditions in sub-Saharan Africa that exposed the region to the dire consequences of the pandemic. Previously existing underlying conditions that affected the food system include a high dependence on trade for inputs supply, the adverse effects of climate change, crop pests and diseases, poverty, low input use, weak institutions and ineffective poli¬cies, and insecurity and conflicts. We collected data from farmers, aggregators, bean research coordina¬tors, and urban and peri-urban consumers in five Eastern African countries in order to describe the immediate impacts of the pandemic on the bean value chain. Access to seed and labor appear to be the most critical impacts of the pandemic on bean production. There are observable differences in patterns and frequency of bean consumption in these regions, suggesting that the effect of the pandemic depends on the level of implementation of containment measures and pre–COVID-19 underlying conditions that affect the food systems. In the mid to long-term, the pandemic may disrupt food systems, resulting in hunger, malnutrition, and food insecurity. Thus, governments should support farmers and businesses in becoming resilient to exogenous shocks through increased efficiency in supply chains, capacity building, and the adoption of modern digital technology.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".