Electoral participation and household food insecurity in sub‐Saharan Africa
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
Abstract This paper analyses the impact of household food insecurity on electoral participation in 30 sub‐Sahara African countries with the aid of micro‐level data drawn from the sixth round of the Afrobarometer survey. Estimates from logistic regression indicate that being food insecure reduces the likelihood of electoral participation by 7%. Notably, results from the endogenous binary‐variable regression, which controlled for potential reverse causality, confirm that household food insecurity is a crucial driver of voter turnout in sub‐Saharan Africa. Further analysis reveals that voting behaviour was much higher and statistically significant amongst voters who were intermittently food insecure than those that were always food insecure. Finally, it appears that turnout at national elections depends mostly on the severity of food insecurity. Therefore, it can be argued that the implementation of policies aimed at stemming household food insecurity could play an essential role in increasing voter turnout.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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