Gender as a Cross-Cutting Issue in Food Security: The NuME Project and Quality Protein Maize in Ethiopia
Bibliographic record
Abstract
Gender research and gender empowerment, particularly through the increased participation of women in extension services and activities, are recommended components in development initiatives toward achieving gender equality, food security, and improved health in rural populations. Gender dynamics have been under-researched in the agricultural technology literature on Sub-Saharan Africa. This article contributes a gender-based analysis of the Nutritious Maize for Ethiopia (NuME) project, an initiative implemented through a partnership among national and international institutes for agriculture and public health. NuME promotes production of quality protein maize (QPM), a group of nutritionally improved or biofortified maize varieties, to improve food and nutritional security. Combining baseline data and case studies of project sites, our analysis illuminates opportunities and obstacles to the adoption and impact of QPM. We find that women in the project face barriers toward the adoption and effective utilization of such technologies. These include less contact with agricultural extension, lower awareness of QPM, and less input into decisions on and key aspects of adoption, production, and marketing. Our findings confirm a link between gender inequalities and food insecurity. We conclude with specific policy recommendations and gender empowerment strategies for governments and implementing partners to improve women's access to agricultural technologies and services.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".