THE EFFECT OF COVID-19 PANDEMIC ON U.S. BEEF EXPORTS TO ITS MAJOR TRADING PARTNERS
Bibliographic record
Abstract
This study analyzed the effect of COVID-19 on U.S. beef exports to South Korea, Japan, China, Mexico, and Canada between June 2017 and July 2023. After accounting for potential factors influencing exports, the study finds that the pandemic’s impact was different in both magnitude and persistence among major trading partners. The drop in exports to Japan and Mexico were notable during the pandemic. The decline in exports began early in the year for Mexico and in March for South Korea. Exports to Canada on the other hand both increased and decreased during this period. Exports to China followed a different pattern and increased during the first six months of the pandemic. Exports to all major export markets recovered as the effects of the pandemic eased by the end of May and fully recovered to pre-pandemic levels by the end of the year. Post-pandemic export volumes have been steady in all major markets amidst the seasonal fluctuations except for Mexico. A confluence of factors including the per capita GDP of the importing country, the exchange rate, trade agreements, time trend, and seasonality influenced U.S. beef exports during the study period. In conclusion, the study reveals the complexity of bilateral trade and importance of accounting for other confounding influences when measuring the impact of the pandemic on U.S. beef exports.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".