African Green Revolution, food sovereignty and constrained livelihood choice in Mozambique
Bibliographic record
Abstract
This article examines the complicated food security agendas of the African Green Revolution and the food sovereignty models in Mozambique. Drawing on fieldwork conducted by the author in Mozambique in 2014 and 2015, the paper analyses how smallholder farmers engage with these two agrarian models. Whereas the literature frequently presents the African Green Revolution and the food sovereignty in oppositional frames, this paper finds that farmers in Mozambique utilize some of the tools that these models offer in complementary rather than competing ways. One such area is the use of commercial hybrid seeds and herbicides by some farmers associated with food sovereignty, an approach that runs counter to food sovereignty’s principles of agroecology. In Mozambique, farmers’ “lived experience” of food sovereignty is more a strategic response to their limited livelihood options, using whatever tools are available to them, rather than a resistance to power.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".