Long-term economic impact of countervailing duties on coated free sheet paper imported by the United States from China, the Republic of Korea, and Indonesia
Bibliographic record
Abstract
The international effects of United States countervailing duties on imports of coated free sheet paper from China, the Republic of Korea, and Indonesia were predicted with the Global Forest Products Model, up to the year 2020. The results indicate that the production of printing and writing paper in China, Indonesia, and the Republic of Korea would be lower. The trade balance would worsen in Korea and Indonesia. China, currently a net exporter would become a net importer. Concurrently, production and prices of chemical pulp would decrease substantially in China. However, because of lower prices in China, its domestic consumption of printing and writing paper would increase. In the United States, the duty would induce little increase in production or improvement of net trade. The main effect would be on the United States' source of imports. While the United States' imports of printing and writing paper from Korea, Indonesia, and China would decrease, the imports from Canada, Finland, Germany, and other sources would increase. Moreover, although imports of printing and writing paper from China would be reduced for a few years by the duty, they would start increasing again after less than a decade. The Canadian industry would gain the most from the duty. Canada's production of printing and writing paper would be nearly 9 percent higher. The United States would see some increases in producer revenues, consumer expenditures, and value-added, but they would be small compared to the increases in Canada.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".