The Improvement of Civil Pretrial Procedures in China: the Comparison of Pretrial Procedures in China, Japan and South Korea
Bibliographic record
Abstract
Although the Civil Procedure Law of the People's Republic of China has provided procedures for pre-trial preparation, such procedures have serious defects based on current legislative and judicial situations. Therefore, they are unable to perform appropriate functions. We therefore analyzed the defects pretrial procedures and discussed a possible reformation of pre-trial procedures based on the successful experiences in other countries and actual conditions in China. Key words: Pre-trial procedure; Time limit for the burden of proof; The exchange of evidence Resume le code de la procedure civile chinoise a defini les preparations de la procedure civile avant l’audiance au tribunal. Cependant, vu les conditions actuelles en matiere de la legislation et de la juridicition, cette procedure demeure gravement defaillante sans pouvoir fonctionner correctement. Le present document anaylyse les defauts existants dans les preparations de la procedure civile prealable chinoise. Compte tenu des conditions actuelles de la Chine et avec les experiences utiles etrangeres, le present document met en avant la discussion sur les preparations prealable de la procedure civile de la Chine. Mots-cles: Procedure civile preaable; Delai pour l'echange de preuves provisoire des procedures d'une preuv; Echange d’epreuves
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".