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Record W2300458837 · doi:10.1002/iir.1248

‘Pre‐Pack Administration Sale: a Case of Sub Rosa Debt Restructuring’

2016· article· en· W2300458837 on OpenAlexvenueno aff
Anthony Wijaya

Bibliographic record

VenueInternational Insolvency Review · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Insolvency and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsRestructuringDebt restructuringCreditorDebtInsolvencyBusinessAdministration (probate law)CorporationFinanceEconomicsLawPolitical science

Abstract

fetched live from OpenAlex

Abstract Under the UK insolvency regime, debt restructuring is ordinarily achievable via a voluntary arrangement, a scheme of arrangement (“scheme”) or a combination of a scheme with administration. However, recently, there has been a growing development of companies using pre‐pack administration sale (“pre‐pack sale”) to effect a debt restructuring under the moniker of a sale of the assets of the company. This article argues that this development poses a genuine danger for the creditors and in particular the junior creditors because such transactions side‐step the protections afforded to the junior creditors in a debt restructuring, particularly a scheme. This article posits that such pre‐pack sales are essentially a sub rosa debt restructuring. Against this backdrop, this article proposes for the use of an ex ante judicial regulatory strategy through the application of the Re Tea Corporation principle to better protect the interest of the junior creditors. Copyright © 2016 INSOL International and John Wiley & Sons, Ltd. Copyright © 2016 INSOL International and John Wiley & Sons, Ltd

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.279
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations0
Published2016
Admission routes1
Has abstractyes

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