A COOL Tale: Economic Effects of the U.S. Mandatory Country of Origin Labeling Repeal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract U.S. Congress repealed Mandatory Country of Origin Labeling (COOL) for beef and pork in December 2015 to avoid retaliatory tariffs from Canada and Mexico. We simulate and compare economic impacts from these retaliatory tariffs with scenarios where COOL was repealed using a global economic modeling framework. Retaliation would have decreased North American trade, decreased U.S. welfare, and increased welfare for Canada and Mexico. Simulated effects of the COOL repeal show modest welfare increases in the United States, Mexico, and globally, with heterogeneous welfare effects for Canada. We discuss whether recent U.S. protectionist policies may lead to similar outcomes to those simulated here.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it