Comparative Study of Foreign Investment Protection Regulations in Shanghai and Shenzhen Special Economic Zones
Bibliographic record
Abstract
One of China's most important tools for achieving economic reform goals, both at the policy-making and legislative levels, has been the use of special economic zones. China's successes in this area have made the study of this significant tool remarkable. In this context, studying the special economic zones of Shanghai and Shenzhen is particularly considerable. This is because, first, the regulations of other special economic zones in China have been influenced by the regulations of these two regions, and second, the economic successes of these two zones have been more remarkable. Therefore, this article aims to explain the background of the establishment of special economic zones in China, review the governing regulations of these two regions, and elucidate their similarities and differences. This research shows that the formation of these zones is based on political and economic reforms of the People's Republic of China in the last quarter of the twentieth century. Likewise, according to the constitution, the 'State Council' is the related responsible institution in this regard. In both regions, the legislature has granted various facilities to foreign investors to protect foreign investment and has deemed the principle of " Pre-establishment National Treatment " essential. Moreover, foreign investors are required to comply with the "negative list for foreign investment." However, significant differences in the policies and regulations governing these two zones can also be observed. For instance, legislators in Shenzhen have emphasized "attracting foreign investment", while legislators in Shanghai have focused on "protecting foreign investment".
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".