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Record W3164742340 · doi:10.1002/iir.1425

Mandatory disclosure in corporate debt restructuring via schemes of arrangement: A comparative approach

2021· article· en· W3164742340 on OpenAlexvenueno aff
Wai Yee Wan, Casey Watters

Bibliographic record

VenueInternational Insolvency Review · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Insolvency and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsDebtorRestructuringCreditorDebt restructuringJurisdictionInformation asymmetryDebtBusinessAccountingLaw and economicsFinanceEconomicsLawPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.274
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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