An Evaluation of Economic Models to Provide Policy Advice in Response to the BSE Crisis in Canada
Bibliographic record
Abstract
The days following the discovery of BSE in Canada were characterized by considerable uncertainty for both policy makers and industry stakeholders. This paper develops eight criteria for economic systems models that would provide information on the impacts of border closures and the effects of programs designed to alleviate some of the financial strain on industry stakeholders. The eight criteria provide a framework to evaluate two economic models used extensively by policy makers in Canada to provide policy guidance in addition to a survey of published economics literature related to the Canadian beef industry prior to the discovery of the BSE case in May 2003. The results suggest a need to improve the combination of knowledge and modeling skills and techniques before another tragedy strikes the agricultural industry in Canada. Les jours qui ont suivi la découverte d'un cas d'ESB au Canada ont semé beaucoup d'incertitude chez les décideurs et les acteurs de l'industrie. Dans la présente étude, nous avons élaboré huit critères pour des modèles de systèmes économiques qui fourniraient de l'information sur les répercussions de la fermeture des frontières et sur les effets des programmes visant à atténuer une partie des contraintes financières auxquelles sont confrontés les acteurs de l'industrie. Les huit critères offrent un cadre pour évaluer deux modèles économiques que les décideurs au Canada utilisent fréquemment pour déterminer une orientation politique, en plus d'un tour d'horizon de la littérature économique sur l'industrie bovine au Canada avant la découverte du premier cas d'ESB en mai 2003. Les résultats ont montré la nécessité d'améliorer la combinaison connaissances, compétences et techniques de modélisation avant qu'une autre tragédie ne frappe l'industrie agricole canadienne.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".