An empirical study of micro‐ and <scp>small‐enterprise</scp> bankruptcy protection under the <scp>COVID</scp>‐19 pandemic: New evidence from China
Bibliographic record
Abstract
Abstract Micro‐ and small‐enterprises (MSEs) serve as a strong foundation for sustainable and stable economic and social development in countries worldwide. However, they are highly susceptible to bankruptcy crises, a phenomenon that has been significantly amplified by the COVID‐19 pandemic. The article presents an empirical investigation of the application of the bankruptcy system to MSEs, using data on MSE bankruptcies published by the Sichuan courts in China in 2020 during the COVID‐19 pandemic as evidence. The article reveals that MSEs face a triple dilemma when applying bankruptcy procedures. Firstly, the legal system for bankruptcy is unsound. Secondly, the institutional mechanism for bankruptcy is imperfect. And thirdly, the social environment surrounding bankruptcy is not conducive. The article presents a novel approach to address bankruptcy issues by suggesting the implementation of streamlined bankruptcy procedures, a reorganization system for small and micro‐enterprises and a personal bankruptcy system. Additionally, it proposes enhancing the institutional framework for the selection, evaluation and oversight of bankruptcy judges and administrators, as well as the establishment of third‐party institutions. Furthermore, it advocates for a modernized understanding of bankruptcy, the provision of pre‐bankruptcy services and the creation of a bankruptcy protection system. The article presents a unique sample of international comparative studies on MSEs and proposes a new approach to examining bankruptcy protection for MSEs in 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".