Economic and legal necessity of personal bankruptcy legislation in China
Bibliographic record
Abstract
Abstract With more domestic pilot explorations launched in China, alternative debt adjustment regulations and bills relating to personal bankruptcy are thriving. This article aims to familiarize the readers with both the economic and legal necessity of a personal bankruptcy system as well as the feasibility of future legislation in China by examining publicized reports and present regulations. Past decades have witnessed the take‐off of China's economy along with prevailing trends in higher debt ratio, more credit loans, and pan‐commercialization. The proliferation of over‐indebtedness propels the need for personal bankruptcy regime. Given the status quo, some alternative legal methods are implemented. By deconstructing the existing regional regulations, this work identifies both progress and drawbacks of current debt adjustment rules from three angles: creditors' benefits, debtors' benefits, and social benefits. After identifying the need for personal bankruptcy legislation, this contribution focuses on the feasibility of a personal bankruptcy system in China. Several salient issues are discussed in three aspects, including political implications, cultural resistance, and social conditions. These unique factors should be considered in forthcoming legislation in China in order to secure a more suitable system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".