Using an intersectionality framework to assess gender inequities in food security: A case study from Uganda
Bibliographic record
Abstract
Abstract Prior research in health equity, including food security, indicates that disadvantaged groups, such as women with limited resources, face many obstacles in achieving food security. One of the first of its kind to draw on intersectionality and the social determinants of health frameworks, this study identified and tested gender differences in experiencing food security inequities using nationally representative data from the Gallup World Poll, Uganda 2019 (N = 951). Binary logit models disaggregated by gender were estimated to identify gender differences in food security. Three points of intersection were categorized: individual characteristics (gender, age, region, marital status, household number of children and adults); available resources (education, income, employment, shelter, social support); and the socio‐political context (community infrastructures, corruption within the business). Testing the moderation effect of gender with each variable (difference‐in‐difference) showed that although most variables correlated with a difference in experiencing food security by gender, only two—marital status, and social support—presented a statistically significant difference. Accounting for this moderation effect, the final model showed that lacking shelter and residing in Eastern Uganda decreased food security. More adults in the household, higher education, higher income, available social support, and satisfaction with community infrastructures enhanced the odds of food security. Results suggest that (a) conventional food security quantitative approaches may not suffice to model inequities when gender is a control variable rather than a foundation to explain inequities; and (b) gendered‐centered analysis helps better identify disadvantaged groups and inform policies that target associated inequities.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".