CHINA'S RAPIDLY CHANGING TRADE AND INVESTMENT INVOLVEMENT WITH THE SOUTH
Bibliographic record
Abstract
AbstractWe present both data on and forward projections of China's rapidly growing trade and investment flows with Southern countries. In this study, we exclude Hong Kong (China), Korea and Singapore. The data indicate that from a low base in the 1990's when Southern trade was growing more slowly than China's overall trade it subsequently grows rapidly. After 2001, (WTO accession) there has been a significant acceleration, which pre crisis was in turn further accelerating. In 1995, Southern trade was 13% of China's total trade; but by 2007 it was 28% of China's total trade and growing at 42% per year on the export side in contrast to 26% for all exports. India (with a 33-fold increase between 1995–2007) is the most rapidly growing bilateral partner, followed by Brazil (18-fold increase between 1995–2007). In Latin America and Africa are the largest regional sources of trade growth, with more dispersion across countries in Asia. In contrast to its total trade, China runs a significant trade deficit in its Southern trade due to imports of resource products. We report projections that by 2015 Southern trade (assuming unchanged growth rates) would be over 50% of China's trade, and by 2025 India will account for over 50% of China's trade.We also present data on Southern and bilateral FDI flows involving China and developing countries. From a very small base in the 1990's, these are now growing at even more rapid rates than China's trade. Bilateral flows between India and China, for instance, grew 90-fold between 1995 and 2007.We finally present data on the initial impact of the financial crisis on China's Southern trade and investment. Southern trade show compression, but this is smaller in percentage keeping in view China's overall total flows. There has been slightly slowed investment of China in the South and the South in China relative to that of total FDI. Despite sharp contraction in some countries (Russia, Singapore), the wider impact elsewhere (Brazil, India) seems to be the dominant effect and Southern relative to North involvement continues to grow for 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".