Gender as a Cross-Cutting Issue in Food Security: The NuME Project and Quality Protein Maize in Ethiopia
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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