New development of cross‐border insolvency in the Chinese Mainland and Hong Kong: Criteria review and jurisdictional issues
Bibliographic record
Abstract
Abstract Since its implementation in May 2021, the Cross‐Border Insolvency (hereinafter CBI) Cooperation Mechanism between Chinese Mainland and Hong Kong has operated for 4 years, yielding a discernible number of cases crossing the two jurisdictions. To facilitate future collaboration, this article evaluates the current practices under the Cooperation Mechanism and identifies existing challenges. Specifically, by analysing cases adjudicated between designated pilot courts in the Chinese Mainland and Hong Kong, the article clarifies the application of key legal criteria, including the Centre of Main Interests (COMI) test, the requirement that “the company's principal assets in the Mainland are located in a pilot area, or it maintains a place of business or a representative office in a pilot area,” and the necessity principle. Furthermore, the article highlights gaps in procedural rules and judicial practice when Mainland courts issue applications for recognition and assistance to Hong Kong courts. Given that the Cooperation Mechanism and its accompanying Practical Guide only outline the manner and procedures for applications within pilot areas, this study also examines practical challenges in cooperation between Hong Kong and non‐pilot areas in the Chinese Mainland. Finally, the article summarizes the policies issued in pilot areas to implemented Cooperation Mechanism and proposes potential solutions to strengthen cross‐border insolvency collaboration.
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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 | 0.082 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".