Practice approaches and future trends in the personal bankruptcy system in China
Bibliographic record
Abstract
Abstract In recent years, the need to enact a personal bankruptcy system has become progressively urgent in China. With increased social acceptance, the practice of personal debt solutions in local courts has been booming. The promulgation of the Personal Bankruptcy Ordinance of the Shenzhen Special Economic Zone in 2021 is an important event of epoch‐making significance in China's bankruptcy legislative history. The revision of the Enterprise Bankruptcy Law 2006 has been included in the legislator's working plan. Whether a personal bankruptcy system could be introduced during the revision has become a major focus. The challenges to personal bankruptcy legislation in China include the public concern that debtors abuse the system to evade debts; the judicial pressure caused by too many cases; and the lack of an out‐of‐court mediation mechanism. The personal bankruptcy system is one of the most inconsistent legal fields in the world. China's future personal bankruptcy law will confine its discussion to the subjects of law, bankruptcy discharge period, and regulation of debtors' debt evasion. Legislators should refer to the advanced legislative experience and set up the personal bankruptcy system by combining it with China's national situation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".