A NEW PERSPECTIVE ON CHINA TRADE GROWTH: APPLICATION OF A NEW INDEX OF BILATERAL TRADE INTENSITY
Bibliographic record
Abstract
This paper analyzes China’s trade relationships using a new trade intensity index, which incorporates gravity model estimation, to compare observed trade levels with levels would be expected to prevail given the economic, geographic, and cultural characteristics of the trading partners. The index is calculated to study China’s bilateral trade intensity, and uses Japan as a comparative case. Standard trade intensity index measures suggest China trades at a very intensive level with countries in East and Southeast Asia (ESA) and at a low level with countries in Europe (EU) and US-Canada (USC). The gravity model based index indicates that China’s level of trade with countries in the ESA region is consistent with levels that would be expected given the countries’ characteristics, while China’s level of trade with EU and USC are greater than one would expect given their characteristics. The new index also reveals insights regarding the evolution of China’s trade partners during the years 1988-2005. The paper’s results suggest the gravity model adjusted trade intensity index can provide a useful analytical tool for identifying strategic or other deviations in trade levels.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| 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".