Intervention analysis of the antidumping investigation on wooden bedroom furniture imports from China
Bibliographic record
Abstract
In recent years, the annual imports of wooden bedroom furniture by the United States have been over five billion dollars, with more than two billion dollars of that coming from China. This trend led to an antidumping action against China in October 2003. Since January 2005, antidumping duties of 0.83% to 198.08% have been imposed on individual Chinese firms. To assess the impact of this antidumping action, intervention analysis was employed to examine the import values of four furniture commodities and the prices of two of them over 1997–2008. China and six other major competing countries were included in the analysis. With regard to import values from China, significant trade investigation effects were identified: the petition announcement generated a positive impact in March 2004; the preliminary less-than-fair-value (LTFV) determination had a negative impact from July to December 2004. However, the final implementation did not show any expected trade duty effect. The aggregate impact of the antidumping action on import values from China over 2003–2008 was approximately equivalent to a 1-month import reduction. The impact on the unit prices for China was insignificant. For the six competing countries, intervention analyses revealed that the antidumping action generated a positive trade diversion effect, with the magnitude smaller than the trade depression effect on China.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".