Gendered Determinants of Food Security Inequities Within Intersectionality Framework: Case Study From Uganda
Bibliographic record
Abstract
Food security (FS) research shows that disadvantaged groups, defined through characteristics such as gender, have less resources, and human and social capital, face more barriers to exercise their rights and are more marginalized. This study aims to determine sources of FS inequities intersecting with gender, measuring their relative significance. We used nationally representative data from Gallup World Poll, Uganda 2019 (n = 951). A binary logit model was estimated at different equity levels before and after accounting for gender differences. Predicted probabilities of FS were used to identify differences between men and women across different determinants. We used the difference-in-difference approach to test the interaction of gender with variables exhibiting a gender difference in magnitude, direction, or significance. We tested whether a) such differences (gender gaps) are statistically significant; b) gender significantly intersects with each of those variables. We computed odds ratios that included significant intersections between gender and selected variables. Despite gender differences in several FS determinants, there was a significant gender gap among low-educated (p < 0.1), low-income, married, and socially supported (all p < 0.05) men and women. Further difference-in-difference analysis showed that gender significantly intersected with social support and marital status (p < 0.05). Accounting for gender variability, the final model showed that residing in the Eastern region, lacking shelter, and being a single woman decreased FS odds. More adults in the household, higher education and income, social support, and satisfaction with community infrastructures enhanced FS status. This is a first attempt to model and test gender differences using a difference-in-difference approach within an intersectionality framework. Results suggest that conventional FS approaches may not suffice to reduce inequities if gender is conceived as a control variable rather than a foundation to explain inequities. Gendered-centered analysis helps identify most disadvantaged groups and inform policies to target inequities. Fonds de Recherche du Québec Société et Culture (FRQSC), QC Canada; McGill University.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 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".