Mandatory Country of Origin Labeling Induced Structural Change of U.S. Meat Products
Bibliographic record
Abstract
Country of Origin Labeling (COOL) for meat products have been a debated subject since its implementation in March, 2009. While advocates of COOL suggest it provides valuable information to consumers, opponents on the other hand claim it imposes unnecessary cost on consumers and distort trade in affected commodities. This paper applies a Source Differentiated Almost Ideal Demand System to estimate mandatory COOL induced Structural Change in U.S. imported meat products. Included in the model is a system of equations setting consisting of beef, pork and lamb. The results show an elastic own price for beef from all countries except Canada. Pork from Canada, Denmark and Mexico; and lamb from Australia and New Zealand; has an inelastic own price. The initial impact of COOL resulted in a decline in the imports of the three meat products. Also, the Pre and Post COOL analyses show declined expenditures on all meat types from all the sources. The chow test performed indicates a structural change in all the meat types from all the sources with the exception of beef from Canada and Pork from Denmark. COOL appears to have had mixed effect on U.S. meat imports based on the source of origin of each meat type.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".