Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Africa's agriculture and food systems were already grappling with challenges such as climate change and weather variability, pests and disease, and regional conflicts. With rising new cases of COVID 19 propelling various African governments to enforce strict restrictions of varying degrees to curb the spread. Thus, the pandemic posed unprecedented shocks on agriculture and food supply chains in Sub Saharan Africa. In this study, we use survey data collected from nine countries in Central, Eastern, and Southern, Africa to understand the immediate impact of COVID-19 on production, distribution, and consumption of common beans, and possible food security implications. Descriptive analysis of data collected from bean farmers, aggregators, processors, bean regional coordinators, and mechanization dealers reveal that COVID-19 and government restrictions had impacted the availability and cost of farm inputs and labour, distribution, and consumption of beans in Eastern and Southern Africa. The immediate impacts were dire in Southern Africa with Central Africa slightly impacted. The production and distribution challenges negatively impacted on frequency and patterns of food consumption in households in Africa. Thus, the pandemic poses a greater risk to food security and poverty in the region. Governments could play a significant role in supporting the needs of smallholder farmers, traders and other actors through provision of subsidized agricultural inputs.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 it