Changes in Chinese wood-based exports to the U.S.: Post Lacey Act amendment
Bibliographic record
Abstract
The recent adoption of timber legality legislation in the U.S. (the Lacey Act 2008 amendment) requiring timber imports be sourced from legally harvested wood could have profound impacts on China's re-exports of manufactured wood products to the U.S. This study examines empirically how Chinese wood manufacturers’ sales to the U.S. have changed in response to the Lacey Act. A sample of 225 Chinese wood manufacturers was drawn from two trade shows in Shanghai, China in 2013. The results reveal that Chinese companies’ awareness of the Lacey Act has played an important role in their decision to export to the U.S. over the last five years. The companies who are less familiar with the Lacey Act tend to withdraw from the U.S. market and focus on domestic market. Also the smaller Chinese companies were more likely to withdraw from the U.S. market in the aftermath of the Lacey Act as compared to their larger counterparts. Finally, the Chinese companies that have increased their imports of raw materials from the U.S. were found to have increased their sales to the U.S. market over the last five years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".