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Record W1513711087 · doi:10.1002/iir.1198

Role of Insolvency Practitioners in the UK Pre‐pack Administrations: Challenges and Control

2012· article· en· W1513711087 on OpenAlexvenueno aff
Bo Xie

Bibliographic record

VenueInternational Insolvency Review · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Insolvency and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsInsolvencyNegotiationImpartialityStatutory lawControl (management)Context (archaeology)CreditorAdministration (probate law)Independence (probability theory)BusinessPublic relationsLawLaw and economicsPolitical scienceSociologyEconomicsManagementFinanceDebt

Abstract

fetched live from OpenAlex

Abstract The pre‐pack administrations (‘pre‐packs’) in the UK have repeatedly been criticised for allowing the exploitation of certain types of unsecured creditors. In this context, the role of the administrators (who are qualified insolvency practitioners) is one of the key elements. This article examines the new challenges brought by the pre‐pack strategy to the conventional role of insolvency practitioners as the administrators. It suggests that the pre‐determination nature of pre‐packs is likely to make the administration proceedings less manager‐displacing in practice than the formal rules would suggest. Although this tendency can be expected to facilitate information gathering during the rescue negotiations, it raises urgent questions with respect to the potential alignment of interests between the inside players that may impair the impartiality of the administrators. In response to such challenges, the article argues that, in spite of the recent proposals of introducing drastic statutory regulation to control the controversy of the pre‐pack practice, a proportionate way is to see how the existing control mechanisms can contribute more in reinforcing the independence of administrators. Copyright © 2012 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 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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.831
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.040
GPT teacher head0.284
Teacher spread0.244 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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