Exploring the Relationship between Corruption and Food Security Status on a Global Scale
Bibliographic record
Abstract
Food insecurity is a global problem that has yet to be properly addressed. Its determinants are part of a greater scheme of food security governance and overall governance. Presence of corruption occurs when there are failures in governance. There are currently no studies exploring corruption and food security, on a global scale, with internationally validated tools. This study aimed to fill this gap in the literature. The main objective was to explore the relation between corruption and food security status on a global scale. Data from 2014 Gallup World Poll (GWP) were analyzed. The sample included 185,341 individuals. Food security status, the dependant variable, was assessed using the Food and Agriculture Organization's Food Insecurity Experience Scale. Corruption, the independent variable, was measured using the GWP Corruption Index. Several statistical analyses formed the basis of the current work. Cross‐tabs and logistic regression were conducted to evaluate the relationship between socio‐demographic characteristics and corruption on food security, using IBM ® SPSS ® version 23, using the complex samples module. Descriptive statistics shows that an absence of perceived corruption was significantly higher in food secure population, when compared to food insecure. Women had higher rates of food insecurity than men. Lastly, higher level of education, higher income and full‐time employment were found among food secure population. All of these results were significant. Findings using a logistic regression model show that food insecurity was significantly higher in a population that perceives corruption (OR 1.192) , after adjusting for other variables. Food insecurity was significantly higher among women (OR 1.123) . Also, individuals who were unemployed (OR 1.077) , or had part‐time employment (OR 1.492) had significantly higher odds of being food insecure. When compared to the high‐income group, low‐income individuals had significantly higher odds of being food insecure (OR 19.498). Finally, in terms of age, food insecurity was higher in the younger population (p=0.03) . The findings of this study help fuel a new approach in the global fight against food insecurity. These findings suggest that amongst diverse population demographics, an absence of corruption has a positive impact on food security. The results of this novel study will promote governmental accountability in regards to corruption, and will contribute to emerging research in the field of food security governance.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".