The Chinese “Oppression” Remedy: Creative Interpretations of Company Law by Chinese Courts
Bibliographic record
Abstract
This is the first detailed study of the Chinese oppression remedy under the PRC Company Law (article 20.1-2). Compared to its U.K., Canadian, and Australian equivalents, the wording of the Chinese remedy is vague, and the Supreme People's Court has not clarified its meaning. Legal scholars have virtually ignored this remedy due to its vagueness and apparent unenforceability, and the Supreme People's Court has not produced any authoritative interpretations to clarify its meaning. Yet Chinese courts have acted pragmatically, building up a body of de facto case precedents to transform this remedy into an effective tool for minority shareholders, both Chinese and foreign (and in some cases companies too), to obtain redress for a broad range of wrongs committed by abusive shareholders. At the same time, the vagueness of the statute has led courts to draw differing conclusions over issues such as who is a proper plaintiff; how the oppression remedy relates to the derivative action; and how the term "shareholder" should be defined. These differences need to be addressed by the Supreme People's Court or by legislative amendment to avoid further inconsistent outcomes for parties involved in intra-corporate disputes in China. Alternatively, the use of case precedents based on online judgment databases should be formalized in China to bring more predictability to statutory interpretation and more consistency among courts throughout the country.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".