Using the Socio-Spatial Framework to Understand the Link Between Disability and Severe Food Insecurity in Nigeria
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
Disability and food insecurity are critical global public health issues, particularly in Nigeria. Approximately 25 million people are currently at risk of food insecurity—a figure expected to rise to 33 million by 2025. Additionally, the Nigeria Demographic and Health Survey reveals that about seven percent of household members aged five and older have some form of disability. Despite these obvious social concerns, limited studies have examined the socio-spatial determinants of severe food insecurity in Nigeria. Using data from the 2021 World Bank’s Living Standards Measurement Study Harmonized Dataset, this cross-sectional study investigates the relationship between severe food insecurity, disability, and other socio-spatial variables at the local administrative level. Multivariate logistic regression and multiscale geographically weighted regression were used to analyze the data. Results show that the prevalence of severe food insecurity (12.1 percent) was higher among households with people with disability than households without. Logistic regression results indicated that households with people with disabilities had three times the odds of facing severe food insecurity than households without people with disabilities. Multiscale geographically weighted regression analysis found that disability spatially predicted severe food insecurity in 42 local government areas in the southwestern region of Nigeria, and the percentage of females at the local government significantly predicted food insecurity in eight local governments in north-central (Benue) and two north-eastern states. Findings offer valuable insights into the food insecurity issues among households with people with disabilities in Nigeria.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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