Value of country of origin labeling information for beef and pork in the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it