Just agricultural science: The green revolution, biotechnologies, and marginalized farmers in Africa
Bibliographic record
Abstract
Contemporary agricultural development has changed in significant ways since the green revolution (GR). Its goals have expanded beyond national development to the achievement of environmental and social goals, and, notably, targeted gains for marginalized farmers. Moreover, advances in molecular breeding have expanded the tools used to achieve such goals. This research examines a prominent agricultural biotechnology program, pest resistant (Bt) cowpea in Burkina Faso, and asks whether and how this program can best achieve its goal of delivering benefits for marginalized farmers. I argue that 2 substantially criticized assumptions of GR-era agricultural development—the scale-neutrality of seeds and the sufficiency of expert technical knowledge—continue to guide the Bt cowpea project and limit its ability to deliver benefits for marginalized farmers. The presence of these guiding assumptions can be located in key programmatic decisions that work at a cross purpose to the project’s social goals, notably (a) the choice of parent variety favoring commercial producers, (b) an absence of institutions to extend adoption and benefits, and (c) a lack of meaningful farmer inclusion. This case adds to a body of research that shows that biological innovations alone—what I call “just agricultural science”—are not sufficient to drive socially just outcomes for marginalized farmers without accompanying social innovations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".