Exploring the Relationship between Corruption and Food Security Status on a Global Scale
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».