BSE, farmers and rural communities: impacts and responses across the Canadian Prairies
Notice bibliographique
Résumé
The emergence of the zoonotic disease, bovine spongiform encephalopathy (BSE) in Canada resulted in a severe agricultural crisis. However, little is known about the ways in which farmers and rural communities were affected. The overall objective of this study is to characterize and better understand the impacts on and responses of farmers and rural communities as they relate to this crisis. Research was undertaken in strata throughout the diverse three Canadian prairie provinces – Alberta, Saskatchewan and Alberta – by employing surveys and focus groups. Results indicated there were numerous direct and ‘spillover’ impacts on farmers and rural communities resulting from the BSE crisis. Declines in cattle prices, herd equity and cash flow, often resulting in the need for bank loans, farm credit or off farm employment, as well as emotional and psychological stress were all experienced by farmers as a result of BSE. Importantly, many additional factors such as adverse weather and market volatility compounded the impacts related to BSE, adding to what was already a crisis situation for many farmers. These impacts were not restricted to farms but, rather, extended into the surrounding community fabric in the form of financial and social stress. Results further indicated government policies contributed to the impacts and the effectiveness of farmer responses related to BSE. A longer-term policy shift that has embraced agro-industrialization and entrenchment into the global marketplace has resulted in clear disparities between the biggest and smallest players in the beef industry and agriculture as a whole. This was illustrated in the ways in which governments responded to the BSE crisis, favouring the needs of the largest farmers and agri-businesses over those of smaller-scale, cow-calf producers. This policy shift and response has left the Canadian beef industry, family farmers and rural communities more susceptible to the emergence of similar future risks. A more inclusive approach to risk research and policymaking that meaningfully involved farmers and their rich, longer-term local knowledge might help mitigate similar risks that will inevitably confront agriculture in the future.
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 | 0,000 | 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 | 0,002 | 0,001 |
| 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 ».