Mandatory disclosure in corporate debt restructuring via schemes of arrangement: A comparative approach
Bibliographic record
Abstract
Abstract Creditors often face significant information asymmetry when debtor companies seek to restructure their debts. In the United Kingdom, it is mandatory for debtor companies, seeking to invoke the courts' jurisdiction to restructure their debts via schemes of arrangement (schemes), to disclose material information in the explanatory statement to enable the creditors to make an informed decision as to how to exercise their votes in creditors' meetings. The English schemes have been transplanted into common law jurisdictions in Asia, including Hong Kong and Singapore. However, due to the differences in the shareholding structures and the kinds of debts that are sought to be restructured in the UK and Hong Kong/Singapore, this transplantation gives rise to the question as to whether information asymmetry is in fact adequately addressed in the scheme process. Drawing from the experiences of Hong Kong and Singapore, we argue that there are three principal concerns in the current disclosure regimes: how debtors disclose the analysis as to the returns pursuant to the best available alternative option if the schemes do not proceed; how debtors disclose advisors' fees; and the equality of provision of information in the scheme process.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".