Value of country of origin labeling information for beef and pork in the United States
Bibliographic record
Abstract
Mandatory country of origin labeling (MCOOL) for fresh meats, fish, nuts and perishable food products in the United States was implemented by the USDA on March 16th, 2009. US trading partners such as Canada and Mexico have been strong opponents of MCOOL due to its trade restrictive nature while other opponents argue that MCOOL has not presented any added value to consumers. These controversies have prompted interest in attaining an accurate measure of the value of the information (VOI) provided by MCOOL. Prior MCOOL research has been conducted to determine consumers' willingness to pay (WTP) for meat from a specific country of origin however, no post-MCOOL research has determined consumers' VOI provided by MCOOL. Beef and pork consumers in two Texas grocery stores were recruited to participate in one of two types of economic field experiments involving real food and real money. Data show that, in the context of the experiment, consumers VOI for MCOOL range from 1.37 to 2.26 per meat shopping experience depending on the method used to elicit the values. However a large proportion of consumers (82%) are unaware of the existence of MCOOL. When this fact is coupled with the way MCOOL has actually been implemented by most retailers, the empirical estimates suggest that the value of origin information for beef and pork is about 0.025/lb.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".