Who you know and when you plough? Social capital and agricultural mechanization under the new green revolution in Ghana
Bibliographic record
Abstract
This paper examines the role of social capital in smallholder agriculture mechanization in Ghana under the ongoing agenda for transformation of African agriculture through the new green revolution. It contributes to the ongoing debate on the potential of social capital in explaining socioeconomic activity over time and space. Drawing on the experiences of smallholder farmers (n = 30) from Navrongo using qualitative interviews and focus group discussions, the paper explores how social capital networks shape mechanized service access and utilization among farmers and highlights the historical background to tractor-based mechanized farming in northern Ghana. Findings reveal how local farmers activate and operate in trustworthy social networks at the community level among themselves and externally with government agencies, traders and development partners to facilitate tractor access. The paper also finds that the withdrawal of government subsidies on agricultural services during structural adjustment in the 1980s created an avenue for private sector entry into the tractor service market. In recent times, the market is a blend of both public and private actors. Given the crucial role of social capital, this paper stresses that apart from economic factors, contemporary agricultural policy should build upon contextual sociocultural networks and the resources inherent in them.
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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 | 0.001 | 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 | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".