The role of corruption in global food systems: a systematic scoping review
Notice bibliographique
Résumé
BACKGROUND: Corruption exists at all levels of our global society and is a potential threat to food security, food safety, equity, and social justice. However, there is a knowledge gap in the role and impact of corruption within the context of the global food system. We aimed to systematically review empirical literature focused on corruption in the global food system to examine how it is characterized, the actors involved, its potential impacts, and the solutions that have been proposed to address corruption in the food system. METHODS: We used a systematic scoping review methodology. Terms combining corruption and the food system were searched in Scopus, PubMed, Web of Science, PsycInfo and Econlit, in October 2021. Two screeners applied a priori selection criteria to screen the articles at the title and abstract and full-text levels. Data was extracted into a charting form and thematically synthesized to describe the types of corruption in the food system, the actors involved, how corruption impacts the food system, and potential solutions. Sankey diagrams and narrative summaries were developed to summarize the included studies and findings. RESULTS: From the 238 included records, five main types of corruption were identified in the global food system: bureaucratic corruption, fraud, bribery, organized crime, and corporate political activity. These different types of corruption spanned across various food system areas, from policy and governance structures to food environments, and involved a wide range of actors. More powerful actors like those in public and private sectors tended to instigate corruption in the food system, while community members and primary producers tended to be impacted by it. The impacts of corruption were mostly negative and corruption was found to undermine food system governance and regulatory structures; threaten health, safety, and food security; and lead or contribute to environmental degradation, economic loss, erosion of trust, social inequities, and decreased agricultural productivity. While solution-oriented literature was limited, the essential role of strong governance, use of technology and predictive modelling methods to improve detection of corruption, and organizational approaches to problem solving were identified. CONCLUSION: Our review findings provide researchers and policymakers with a comprehensive overview of corruption in the global food system, providing insights to inform a more holistic approach to addressing the issue. Addressing corruption in the food system is an essential element of supporting the transition to a more healthy, equitable and sustainable global food system.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».