An Empirical Study on International Market Forces of China's Iron and Steel Industry
Bibliographic record
Abstract
This paper uses the marginal cost model to estimate international market power of China's iron and steel industry and finds that international market forces of China's iron and steel industry have dropped after joining the WTO, and international market forces of China's iron and steel industry were small before and after China's joining the WTO. Then it selects the iron and steel of the SITC(Rev.2) 67 and uses the expand residual demand elasticity model to empirically study their international market forces in the Republic of Korea, India, Japan and the United States. It is found that market forces are in turn reduced in the Republic of Korea, Japanese and Indian markets, and market forces have not yet formed in the U.S. market. It indicates that China's iron and steel products export has a certain amount of international market forces, and the ratio of export price and import price tends to decrease and low-cost advantages have gradually weakened. Comparative interests in trade obtained by China's iron and steel products have gradually reduced. Real international market forces of China's iron and steel products are not obvious.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".