Categorization of drivers of change for emerging food safety risks
Notice bibliographique
Résumé
The emergence of food safety risks can be influenced by various causes, also known as drivers of change. Understanding the characteristics related to the manageability of these drivers and the health impact of their associated hazards is critical for effective food safety management and resource allocation. This study aims to categorize drivers of change for known food safety hazards based on their impact on human health and their manageability. Identified drivers were categorized and ranked through an expert survey and a PROMETHEE multi-criteria analysis. The weighted performance criteria are controllability and volatility of the driver, and likelihood and severity of the associated hazards. Results show that the severity of associated hazards is the most important criterion, while volatility of the driver is considered least important by experts. All drivers of change were ranked based on their potential impact on food safety and categorized in a driver matrix with four categories. Categorization is based on the combined effect of expected negative health impact (low or high) and manageability (difficult or easy). The four categories are: "monitor and adapt" (low, difficult), "analyze and optimize" (low, easy), "leverage and innovate" (high, easy), and finally "strategize and endure" (high, difficult). Two drivers-environmental contamination and geopolitical conflict-are categorized as difficult to manage and associated with highly likely and severe health consequences. These drivers represent critical challenges requiring long-term strategic efforts. Six drivers are highly manageable (low health impact: legislation, policies and governance, technologies in food production, food processing technologies; high impact: management of natural resources, bioprocesses, supply chain) and could be leveraged to influence food safety hazards. The results are shown to be robust against changes in criteria weighting. These findings can support decision makers in shifting focus toward manageable drivers and tailoring strategies by driver category. For effective action, it is essential to understand the specific risks and benefits of each driver and the burden of their associated hazards. Future work should explore the top-ranked drivers and integrate these insights into holistic food safety management strategies.
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 | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».