Analysis of China’s Import from & Direct Investment in ASEAN—Based on Gravity Models
Bibliographic record
Abstract
China and Southeast Asian nations free trade area was formally established on January 1, 2010. By the end of year 2011, China’s foreign exchange reserves exceeded 3200 billion US dollars. Trade gravity model originates from the law of universal gravitation. Domestic researchers have made empirical studies using trade gravity model of trade between China and Southeast Asian nations. There are also studies of foreign direct investment or Chinese tourist arrivals in Vietnam, using gravity model. However, neither tariff and foreign exchange reserves are taken into consideration in studying China and Southeast Asian nations free trade area, nor gravity model is used in analyzing China’s direct investment and tourist arrivals in Southeast Asian nations. Using econometrics software “Eviews 5.0” and based on a panel data from year 2000 up to 2008, this paper constructs a gravity model, the independent variables of which are gross domestic product per capita of CAFTA countries, foreign exchange reserves of CAFTA countries, squares of southeast Asian nations, and distance between China and southeast Asian nations. And quantitative relationship is made between independent variables and China’s direct investment in Southeast Asian nations, import from Southeast Asian nations, as well as arrivals of Chinese visitors in Southeast Asian nations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".