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Record W2403275483

MCCOOL and the Politics of Country-of-Origin Labeling

2012· article· en· W2403275483 on OpenAlexaffabout
Alexander Moens, Amos Vivancos Leon

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsFraser Institute
Fundersnot available
KeywordsBusinessProduct (mathematics)Country of originDeclarationValue (mathematics)International tradeCommerceAgricultural scienceAgricultural economicsMarketingPolitical scienceEconomicsBiologyLaw
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.107
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0210.042
Scholarly communication0.0220.018
Open science0.0030.011
Research integrity0.0220.028
Insufficient payload (model declined to judge)0.0120.003

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.

Opus teacher head0.007
GPT teacher head0.200
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes2
Has abstractyes

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