On the Establishment and Application of Broad Prohibition of Repeated Suits in Chinese Law – With “The Requests in the Latter Lawsuit Essentially Deny the Judgement in the Former Lawsuit” as the Object
Bibliographic record
Abstract
Article 247 of the Interpretation of Civil Procedure Law stipulates the prohibition of repeated suits, but it only refers to the narrow prohibition of repeated suits. Its identification elements are "the same parties, the same subject matter of claims and the same requests of the lawsuit". However, there is a widespread phenomenon in judicial practice that the court rejects the latter lawsuit by using the element “the requests in the latter lawsuit essentially deny the judgement in the former lawsuit”. Through the analysis of legal hermeneutics, this element can be used when the former lawsuit is in proceeding and should be understood as "if the latter lawsuit is made, the judgement of latter lawsuit may deny the judgement of the former lawsuit". Therefore, Article 247 of the Interpretation of Civil Procedure Law establishes the broad prohibition of repeated suits. The identification element of broad prohibition of repeated suits should be that the main points of contention of the two lawsuits are the same. With regard to the treatment of the broad repeated suits, the latter court can directly reject it, but should fulfill the obligation of “addition of lawsuit, alteration of lawsuit or counterclaim can be put forward in the former lawsuit” to the parties.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".