Bibliographic record
Abstract
Southeast Asia is a promising market for agricultural exports, with its growing population of middleclass consumers, especially for exporters such as the United States. The region consists of: Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand, Timor-Leste, and Vietnam. Top markets in the region for U.S. agricultural and food products are the Philippines, Vietnam, and Indonesia. Leading U.S. exports are soybean products, wheat, cotton, skim milk powder, and distillers’ grains. U.S. agricultural exports to Southeast Asia increased from $9.4 to $14.2 billion from 2012 to 2022, and the U.S. share of Southeast Asia’s agricultural imports was steady at just over 11percent. China and Brazil, two of the top competitors, were the only exporters that gained market share over the period. China surpassed the United States to become the largest foreign supplier of agricultural goods to Southeast Asia, but few of China’s products compete directly with U.S. products; Brazil’s soybean products, cotton, poultry, and beef do compete with U.S. products. There are numerous potential reasons that U.S. competitors have gained market share, varying by commodity. They include preferential treatment through trade agreements, along with price competition, geopolitical ties, and geographic distance from Southeast Asia. Currently, the primary U.S. competitors for major agricultural commodities exported to Southeast Asia are Brazil, Australia, New Zealand, the European Union, China, India, Canada, and Argentina
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".