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Record W4389790378 · doi:10.1002/iir.1523

Debt restructurings, debt grifting and the limits of contractualism

2023· article· en· W4389790378 on OpenAlexvenueno aff
Gerard McCormack

Bibliographic record

VenueInternational Insolvency Review · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicState Capitalism and Financial Governance
Canadian institutionsnot available
Fundersnot available
KeywordsRestructuringBankruptcyDebtContext (archaeology)KingdomPosition (finance)PoliticsDebt restructuringEconomicsPolitical scienceAccountingLaw and economicsLawFinance

Abstract

fetched live from OpenAlex

Abstract This article critically examines corporate restructuring plans and schemes in the United Kingdom and United States and third‐party releases in the context of such corporate restructurings. So far, the practice has been more extensively examined in the United States rather than the United Kingdom and the practice has been castigated as ‘debt grifting’, that is, third parties getting the benefit of a bankruptcy discharge without going through the formal bankruptcy process. This article acknowledges some of these criticisms. It also suggests that, if third‐party releases become more widespread in the United Kingdom, this is likely to militate against the success of the United Kingdom as an international corporate restructuring venue. This is particularly the case if the underlying debt is disputed or gives rise to social or political controversy. The article is divided into five parts. After the first introductory part, the second part will examine how debts are restructured in the large corporate context in the United Kingdom and how third‐party releases are important for this endeavour. The third part will examine the equivalent position in the United States. The fourth part explores how the restructuring solutions currently on the table push up against the limits of contractually derived solutions. The final part concludes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.029
GPT teacher head0.273
Teacher spread0.243 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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