Building bridges in China for a more harmonious society: Origins and evolution of personal insolvency in Taiwan and Shenzhen
Bibliographic record
Abstract
Abstract A much‐heralded new law in Shenzhen, China, is not nearly as new as it might appear. The law's past offers revealing and important lessons for its successful future. Effective from March 2021, the ground‐breaking Shenzhen personal bankruptcy regulation is an impressive monument of Chinese legislation, but, contrary to what has been commonly announced, it is not the first Chinese personal insolvency law. Rather, Shenzhen's statute was founded on, and still reveals vestiges of, a predecessor law with a rich background of informative development. The origin law emerged 13 years earlier across the Strait in Taiwan. Although it was originally based on this Taiwanese law, the Shenzhen successor has powerfully evolved to take into account lessons from earlier struggles in Taiwan and the rest of the world, as catalogued in a landmark report by the World Bank. The Shenzhen model thus embodies an especially elegant product of building bridges from common experience in both East and West leading toward common goals. This article charts the progression of policy choices and results in Taiwan, through the decades of trial and error analyzed in the World Bank report, and culminating in the state‐of‐the‐art new Shenzhen regulation, which is poised for potential rollout to all of Mainland China.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".