IMPACTS OF U.S. COUNTRY OF ORIGIN LABELING ON U.S. HOG PRODUCERS
Bibliographic record
Abstract
Country of Origin Labeling (COOL) is a component of the 2002 US Farm Bill. The provision requires that fresh meat and produce be labeled as to the country of origin at retail in the United States. For a variety of reasons that have been addressed in previous research projects, (Meyer and Hayes for example) COOL could potentially result in the reduction or elimination of the trade in livestock between Canada and the United States. More particularly, for the purposes of this project, COOL could eliminate the annual movement of up to 6 million hogs from Canada to the United States. The purpose of this project is to identify the possible economic, structural and social damage COOL could inflict directly on US hog farmers and processors if the imports of Canadian hogs were stopped. In order to achieve that purpose, the project had the following objectives: 1. Search and examine existing research on the impact of COOL in the United States. 2. Profile US farms purchasing Canadian weanlings. 3. Determine the economic disadvantage from the loss of Canadian weaner imports due to COOL. 4. Determine the social, environmental and economic cost of building U.S. sow units. 5. Determine the economic disadvantage on the US pork packing sector. 6. Determine the potential hog price impact. 7. Examine cost of compliance issues.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".