Country-of-Origin-Labeling Creates Winners and Losers among Consumers, Producers, and Retailers
Bibliographic record
Abstract
Public Law 107-171, the United States Farm Security and Rural Investment Act of 2002, required country-of-origin labeling (COOL) for beef, lamb, pork, fish, perishable agricultural commodities, and peanuts. While the aim is to benefit domestic consumers by allowing them to make informed decisions, the effects of COOL have been the subject of a heated on-going debate. Advocates of COOL argue the existence of an “overwhelming” consumer support for country of origin information and benefits that substantially outweigh the costs of this labeling regime. Opposing groups have expressed concerns about the potential competitive disadvantage that non-integrated producers might face due to higher record-keeping costs. Public Law 110-246 of 2008 modified provisions regarding recordkeeping, labeling products of multiple origins, and penalties for noncompliance, and increased the number of commodities covered by the policy. This did little to end the debate about the policy, however. Canada and Mexico have challenged the legality of COOL within the World Trade Organization (WTO) in December 2008. A WTO dispute settlement panel found certain aspects of the COOL requirements, as they apply to beef and pork, to breach the WTO commitments of the US. The panel’s report has been appealed by the United States (March 23, 2012), and by Canada and Mexico (March 28, 2012).
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 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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".