Agricultural Trade among NAFTA Countries: A Case Study of U.S. Meat Exports
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
The U.S. NAFTA partners are important markets for U.S. meat exports. A source-differentiated almost ideal demand system is used in this study to estimate meat demand in Canada and Mexico. Empirical results suggest that while a U.S. price increase in the Canadian market is expected to increase U.S. sales revenues; it would decrease sales revenues in the Mexican market. Furthermore, an increase in meat expenditures in Canada and Mexico is expected to increase the demand for U.S. meats, while the bovine spongiform encephalopathy outbreaks have had a negative effect on U.S. and Canadian beef market shares. Finally, a decomposition of the causes of changes in demand for U.S. meats over time is performed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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