Women's Empowerment and Food Security Status: A Global Comparative Study of Women Living in Rural and Urban Areas
Bibliographic record
Abstract
In recent decades, many efforts have been made to empower women, nonetheless, many gaps in gender equality still exist. These gender disparities have negative consequences not only for women but also for their families and society as a whole. Empowering women plays a vital role achieving and maintaining a food secure state. The objective of this study was to examine the association between indicators that are considered to contribute to women's empowerment and food security (FS). A sample of 15,334 rural women and 10,098 urban women was obtained from the 2015 Gallup World Poll, which is a nationally representative survey of individuals 15 years and older in 150 countries. The Food Insecurity Experience Scale within this dataset was used to categorize by FS status (i.e. Secure and Insecure) as the dependent variable, while women's empowerment indicators were used as independent variables. Crosstabs analyses and binary logistic regressions, for both rural and urban areas, were carried out using SPSS 23 Complex Sample. Several socio‐demographic characteristics, including education, marital status, employment status and income, were found to show significant differences between women living in rural and urban areas by FS status. Also, rural women were at a higher risk of being food insecure. Findings from the logistic regression show that rural women with higher levels of social support ( OR 0.697 ), education ( OR 0.175 ), control over life ( OR 0.513 ), and personal health index ( OR 0.402 ), had significantly higher odds of being food secure. Similar results were found among women living in urban areas in terms of education ( OR 0.241 ), personal health index ( OR 0.209 ) and control over life ( OR 0.445 ). Finally, urban women who perceived being treated with respect ( OR 0.438 ) also had higher odds of being food secure. The results confirm that the determinants of food security are highly complex, they do not act autonomously, and their effects are intertwined. Nonetheless, it is clear that factors contributing to women's empowerment play a key role in FS. Women's access to education is invaluable and should be prioritized worldwide as a means of improving FS. Shifting social norms to allow women to take control of life decisions is essential in achieving FS. Insuring one is able to maintain a good health status is pertinent to FS. As other studies have shown, strengthening social support is also a valuable tool to combat food insecurity, especially among rural women. Finally, living in urban areas provides increased and diverse opportunities for women compared to living rural areas. Addressing this opportunity gap in the future will be helpful in ensuring women's empowerment and FS for all.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".