Women's agency in nutrition in the association between women's empowerment in agriculture and food security: A case study from Uganda
Bibliographic record
Abstract
Abstract This study examined the relationship between women's empowerment in agriculture (WEA), women's agency in nutrition, and their food security. It aimed to quantify the moderating effect of women's agency in nutrition on the association between WEA and food security. Data from the NutriFish project, a gender‐ and nutrition‐sensitive agricultural intervention in fishing villages in Uganda, were utilized. The study included 380 primary Ugandan female decision makers in dual adult households. WEA was measured using the Project‐level Women's Empowerment in Agriculture Index (pro‐WEAI). Women's agency in nutrition was assessed through measures of agency in regular diet, pregnancy diet, breastfeeding diet, and food purchase. Binary logit regression models were employed to estimate differential associations between WEA and food security, testing three‐way interactions between WEA, agency in regular diet, and food purchase. Results showed that WEA was associated with a 0.18 increase in the predicted probability of food security (p < .01). Women's participation in food purchase decisions strengthened the WEA‐food security association by 0.33 (p < .05). The results suggested that promoting women's food purchase agency can enhance the positive link between WEA and food security. Prioritizing interventions empowering women in food purchase decisions improves food security in gender‐ and nutrition‐sensitive programs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".