This Little Piggy Went to Market with a Passport: The Impacts of U.S. Country of Origin Labeling on the Canadian Pork Sector
Bibliographic record
Abstract
This paper examines the potential impacts of U.S. mandatory country of origin labeling. North American hog and pork markets are represented as vertically related in a partial equilibrium non‐spatial model. A synthetic model is calibrated to historic data and then used to trace the program's added costs as they are passed through the market to determine who wins and who loses. The transactions costs reduce the welfare of all agents in the United States. Canadian welfare depends primarily on whether mixed supply chains continue to be accepted in the United States and trade in hogs continues. A closed border significantly reduces the welfare of Canadian hog producers and increases the welfare of Canadian pork processors. Le présent article porte sur les répercussions éventuelles de l'étiquetage obligatoire du pays d'origine réclamée par les États‐Unis. Les marchés nord‐américains du porc vivant et de la viande de porc sont représentés comme étant verticalement liés dans un modèle non spatial d'équilibre partiel. Un modèle synthétique est étalonné avec des données historiques et est ensuite utilisé pour déterminer les coûts supplémentaires qu'occasionne le programme à mesure qu'ils sont répartis sur le marché pour déterminer les gagnants et les perdants. Les coûts de transaction diminuent le bien‐être de tous les agents aux États‐Unis. Le bien‐être au Canada dépend principalement de l'acceptation ou non des chaînes d'approvisionnement mixtes et de la continuité ou non du commerce du porc vivant. La fermeture des frontières diminue considérablement le bien‐être des producteurs de porcs canadiens et augmente celui des transformateurs de porcs canadiens.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 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".