A COOL Repeal: Potential Outcomes of U.S. Mandatory Country of Origin Labeling Requirements on Dairy and Beef Sectors
Bibliographic record
Abstract
US Congress repealed the Mandatory Country of Origin Labeling (COOL) requirement for certain agricultural commodities on December 18, 2015 after continued controversy within the US agricultural industry and seven years of consultations in the World Trade Organization (WTO) Dispute Settlement (DS) process. The COOL repeal followed the WTO final ruling authorizing retaliatory import tariffs totaling $1 billion on US-sourced imports into Canada and Mexico beginning on December 21, 2015. While the dispute was based on the trade-distorting effects of COOL on Canada and Mexico’s beef and pork exports to the US, both trade partners announced intentions to retaliate with tariffs on imports of a suite of US-sourced agricultural commodities, including dairy and beef products. This paper evaluates the potential outcomes of the COOL repeal on directly and indirectly affected industries. Specifically we will investigate two scenarios; first, the potential economic effects of continued COOL, assuming Canada and Mexico are permitted to retaliate against agricultural imports from the US, and second, the potential outcomes of removing mandatory COOL in agricultural sectors.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".