Lessons from the old Green Revolution for the new: Social, environmental and nutritional issues for agricultural change in Africa
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
Recent efforts for an ‘Alliance for a Green Revolution in Africa’ (AGRA) promote fertilizer, hybrid seeds, pesticides and biotechnology to increase agricultural production. This article examines the original Green Revolution to understand potential effects of a recent promotion of related technologies in Africa. Using a case study of Malawi, the implications of promoting high-input, intensive agriculture on food security, social relations and nutrition are considered. I argue that unless social inequalities and environmental concerns are taken into account, these technologies will intensify inequalities, increase environmental degradation and exacerbate malnutrition for the rural majority, while benefitting the urban poor, larger-scale farmers, agro-input dealers and transnational corporations involved in agribusiness.
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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) | 0.000 | 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 it