A bitter pill: smallholder responses to the new green revolution prescriptions in northern Ghana
Bibliographic record
Abstract
Through a qualitative case study from the Northern Region of Ghana, this paper examines smallholders’ perceptions of environmental change and contemporary Green Revolution prescriptions promoted by foreign donors, NGOs and the state. These prescriptions seek to commercialise and intensify production in Ghana through increasing use of chemical fertilisers, pesticides and faster-growing seed varieties. We argue that many smallholders are reluctantly adopting the inputs and techniques of the Green Revolution in response to erratic rainfall, shortened growing seasons, and drier soils with diminished fertility, as well as other structural constraints such as increasing competition for land. Farmers’ responses are also influenced by social inequalities, as gender and wealth differences affect access to agro-inputs and participation in farm contracts. Female farmers are especially disadvantaged in adoption, which is serving to widen disparities. However, even those farmers who are following the Green Revolution prescriptions are not uncritical of its impacts, as they commonly described their decision to adhere to this technical package as a short-term trade-off to meet subsistence needs at the expense of worsening soil health and increasing debt.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".