Bankruptcy in the time of <scp>COVID</scp>‐19: Special measures adopted by the <scp>People's Republic of China</scp> courts during the period of <scp>COVID</scp>‐19 prevention and control
Bibliographic record
Abstract
Abstract Business bankruptcy in China is governed by the Enterprise Bankruptcy Law (EBL), a national insolvency code enacted in 2007. The EBL contains provisions for business liquidation, reorganization, and compromise of debt. Although adjustment of debt through bankruptcy is far less common in China than in western nations, Chinese courts have established a body of bankruptcy procedures and judicial interpretations that give insolvency in China a measure of predictability and effectiveness. Notwithstanding the EBL provisions, soon after the onset of the pandemic, PRC courts began to adopt ad‐hoc rules and guidelines in bankruptcy cases for businesses whose financial woes were caused or exacerbated by coronavirus, or for enterprises that produced medical equipment and supplies to help prevent and control the virus. This paper examines these court measures, explores their political and judicial context, and demonstrates how they produced bankruptcy outcomes that were often significantly different than what would have resulted if the EBL had been applied based on pre‐COVID‐19 EBL practices.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".