The Empirical Research on Affecting Factors of Foreign Trade Developing in 10 Cities in Zhejiang Province from 1997 to 2006
Bibliographic record
Abstract
The outbreak of the U.S.financial crisis,happened during 2007-2008,had a serious impact on Zhejiang——one of biggest China's foreign trade provinces.Though all the cities in Zhejiang share the same trade pattern,the impact to each city shows a huge difference.In this paper,we make empirical analysis on the factors affecting trade development for 10 cities of Zhejiang from 1997 to 2006 and find that the foreign trade in Zhejiang depends much more on the GDP,the investment in the fixed assets and the industry investment rather than the FDI.This means that the best way to protect itself against the financial crisis is not to depend on the foreign capital but to enhance the competitiveness of its industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".