Canada’s Changing International Trade Landscape – Opportunities, Threats and Forgone Opportunities for the Beef Industry
Bibliographic record
Abstract
Canada’s international trade landscape under which the Canadian beef industry must operate may change in important ways in the near future. The Trans Pacific partnership agreement provided additional opportunities for the beef industry, but those may now represent opportunities forgone in the wake of the recent US election. During the US election, there were explicit references to either renegotiating NAFTA or “tearing it up”. This thesis provides an analysis of the various possible effects on Canadian beef trade based on a variety of trade agreement outcome scenarios. The partial equilibrium Global Simulation Analysis (GSIM) model (Francois and Hall,2003) was adapted to undertake the analysis. This single product, multi-region model provided trade and welfare results that can be compared between scenarios that depend upon the retaining of NAFTA and the potential evolution of the TPP.Le paysage de commerce international dans lequel doit opérer l'industrie canadienne du bœuf pourrait changer de manière importante dans un avenir très rapproché. Le Partenariat transpacifique fournit des occasions supplémentaires pour l'industrie du bœuf qui pourraient maintenant devenir des occasions manquées étant donné les récentes élections américaines. Durant cette élection, la possibilité de modifier ou d'annuler l'ALENA a été soulevée explicitement. Cette thèse fournit une analyse des effets possibles de divers scénarios de dénouement concernant les ententes de commerce sur l'industrie du bœuf canadien. Le modèle d'équilibre partiel d'analyse de simulation mondiale (François et Hall, 2003) a été adapté afin d'entreprendre cette analyse. Ce modèle multirégion à produit unique fournit des résultats sur le commerce et le bien-être pouvant être comparés aux scénarios qui dépendent du maintien de l'ALENA et l'évolution potentielle du PTP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".