Differentiated agrarian vulnerabilities and generalized national responses to COVID‐19 in the Upper West Region of Ghana
Bibliographic record
Abstract
The experiences of COVID-19 differ at both micro and macro levels. This emphasizes the need for differentiated responses that account for the varying vulnerabilities of diverse groups regarding the pandemic. In Ghana, much of the attention on COVID-19 has been on urban centres, particularly the country's two largest metropolises in southern Ghana. This has created a gap between national level policy and the experiences of COVID-19 among rural dwellers in Ghana. This is despite evidence that the world's poorest populations will bear the brunt of COVID-19 effects, and that globally, four out of five people living below the poverty line reside in rural areas. Using the Upper West Region as a case study, we discuss the differentiated vulnerabilities that agrarian communities in Ghana face regarding the pandemic. We situate our discussions within the theories of vulnerability and feminist political economy to highlight how interlocking vulnerabilities regarding historical, environmental, geopolitical, socio-economic, health, and gendered inequalities affect the disposition of agrarian communities to cope with and recover from the COVID-19 pandemic. We call for more nuanced COVID-19 responses that account for the needs and experiences of agrarian communities in Ghana.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".