MCCOOL and the Politics of Country-of-Origin Labeling
Bibliographic record
Abstract
In the United States, Mandatory Country-of-Origin Labeling (MCOOL) was brought into force in 2008. Because of the bill, American retailers must inform consumers about the country of origin of various classes of meat products including muscle cuts of beef, pork, and lamb, as well as chicken, fish products, and other perishable food items.The common practice in most countries is that imported products are either labeled with a simple declaration of their country of origin, or are labeled under the name of the country that has added the last substantial amount of value (such as processing) to the product. The MCOOL provision is substantially different. It requires retailers to use one of four types of labels. In the process of determining the appropriate label, the origin of the animal, where it was raised, and the country in which it was slaughtered and processed must be determined, tracked, and recorded. Over the past several decades, Canada and the United States (as well as Mexico) have developed an integrated supply chain for many red meat products in which calves and young pigs may be born in one country, raised in another, and/or slaughtered on either side of the border. Because of this, the new MCOOL label imposes by necessity a tracking, segregating, and recording system that adds significant extra cost to the integrated system of meat production.This extra cost threatens the efficiency created over the years between Canada and the United States (and Mexico). Producers can now choose an “all-American-all-the-time” product and in so doing avoid steep labeling costs. Contrary to what many legislators suggest — this product is not necessarily of better quality, or derived from a safer animal or a better health standard, but just happens to have cheaper transaction costs due to the criteria and processes needed to implement MCOOL.Since MCOOL went into force, Canadian cattle and hog exports to the United States have decreased by 42 and 25 percent respectively. This drop in trade affects the US nearly as much as it affects Canada as many American processors and packers are faced with a lack of supply. There is an additional impact on employment. The livestock industry directly contributes to over 100,000 jobs in Canada and indirectly to many others. Likewise, many jobs in the United States are jeopardized by this measure.
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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 | 0.023 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.042 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.022 | 0.028 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".