Indirect effects of United States country of origin labelling on the Canadian chicken industry
Bibliographic record
Abstract
The indirect effects of the U.S. country of origin labeling (COOL) implementation on the Canadian chicken industry are assessed using a multi-market commodity model under the partial equilibrium framework. Implementation of COOL in the U.S. red meat sector is simulated on the model through an exogenous demand shifter at the retail level. Simulation results are compared against base line values to assess the impact of U.S. COOL under short run and long run scenarios and also under NAFTA and WTO minimum market access commitments (MAC). Results suggest that COOL increases demand for chicken at the retail level, subsequently raising retail price. The retail price increase is transmitted to the other market levels within the chicken industry, hence increasing chicken prices at the retail, wholesale and farm levels. The larger the increase in the red meat prices in Canada due to COOL, the greater the increase in demand for chicken and prices. All market participants in the Canadian chicken industry gains from COOL under the NAFTA MAC. When the import policy is changed from NAFTA to WTO, COOL brings varied impacts on chicken prices and quantities. Even though COOL has a comparatively larger overall welfare gain under the WTO MAC, Canadian chicken producers and processors are worse off while retailers and consumers are better off from COOL.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".