Does the New Alliance for Food Security and Nutrition impose biotechnology on smallholder farmers in Africa?
Bibliographic record
Abstract
Almost one in three people who live in sub-Saharan Africa (SSA) are hungry, higher than anywhere else. This magnitude of food insecurity coupled with slow progress in regional integration, disease and epidemics, poor access to markets, gender disparities, lack of land tenure rights, and governance and institutional shortcomings on the continent have been used to justify a narrative for the inclusion of biotechnology in smallholder agriculture in SSA. The fact, however, suggests that even in the face of these challenges, smallholder farmers in SSA still produce 70% of the food on the continent. We critically examine the introduction of biotechnology in smallholder farming within the context of the New Alliance for Food Security and Nutrition and public–private partnerships in SSA. We explicitly address the bioethical concerns and implications for technology adoption goals in line with a neoliberal economic model that is encouraging smallholder farmers to adopt biotechnology as a way to secure more food for communities. This paper is not meant to pose a simplistic pro or anti stance on genetically modified (GM) crops or biotechnology, but rather to situate the debate about GM technology within issues of power, control in the global food agriculture systems, and point to the bioethical concerns that affect the lives of smallholder farmers and their families on a daily basis.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".