Country of Origin Labeling and Structural Change in U.S. Imports of Canadian Cattle and Beef
Bibliographic record
Abstract
Mandatory country of origin labeling (COOL) has become a thorny issue in U.S.–Canada bilateral trade relations. We undertake an ex post investigation of the impact of the law on U.S. imports of Canadian beef, feeder, and fed cattle. Using a partial equilibrium framework, we derive U.S. import demand equations for Canadian cattle and beef, and employ the Bai and Perron (1998, 2003) procedure for detecting multiple structural breaks with break points being endogenously determined. We find evidence that COOL may have caused significant structural change in U.S. imports of Canadian feeder and fed cattle. L’étiquetage du pays d'origine obligatoire est devenu un sujet épineux des relations commerciales entre le Canada et les États‐Unis. Dans la présente étude, nous effectuons une analyse ex post des répercussions de la Loi sur les importations américaines de viande de bœuf, de bovins d'engraissement et de bovins finis. À l'aide d'un modèle d’équilibre partiel, nous avons dérivé des équations de demande d'importation de bovins et de viande de bœuf de la part des États‐Unis et nous avons utilisé les tests de Bai et Perron (1998, 2003) pour déceler les ruptures structurelles multiples, dont les points de rupture ont été déterminés de façon endogène. Les résultats de notre étude montrent que l’étiquetage du pays d'origine peut avoir causé un changement structurel considérable sur les importations de bovins d'engraissement et de bovins finis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".