Financial Environment, City Distance and International Operation of Chinese Enterprises
Bibliographic record
Abstract
This paper uses the 9741 internationally-operated Chinese companies and their matched companies in Shanghai and Shenzhen A-shares as a sample to empirically examine the relationship between financial environment, distance and international operation. First of all, the study found that there is a significant positive correlation between the financial environment and the international operation of the company. In other words, the better the financial environment in which the company is located, the more likely it is that the company will conduct international operations. Second, there is also a significant positive correlation between distances and international operations, which means that the closer the geographical location of the registered place of a company and the central city of the province, the more likely it is that the enterprises within the jurisdiction are operating internationally. Furthermore, the urban distance can produce a ‘regulatory effect’ between the financial environment and the international operation of the enterprise. That is the positive correlation between the financial environment and the international operation of the enterprise depends on the ‘city distance’. Considering the influence of ‘city distance’, the positive impact of the financial environment on international operations is even more pronounced.
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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 | 0.001 |
| 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 | 0.001 |
| 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".