Enlightenment of American Legal Theory and Practice of "Piercing the Corporate Veil" to China
Bibliographic record
Abstract
Observing the current legal system and theory of America, "piercing the corporate veil" is in a state of "chaos" in both of them. How can China learn from the rule of law and the theory of "piercing the corporate veil"? How to avoid its harm and gain its benefits? Due to different national conditions and judicial systems, also differences between civil law system and common law system, and at the same time, the world is in the era of globalization, the exchange of legal culture of Chinese legal system is expanding and deepening day by day. Therefore, it is necessary to study the rule and theory of "piercing the corporate veil". This paper systematically summarizes the current situation of the rule and theory of "piercing the corporate veil" in America, explores the causes of the confusion, and puts forward some suggestions to prevent the occurrence of problems in China after transplanting this rule, which is very necessary and timely.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| 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".