Role of Insolvency Practitioners in the UK Pre‐pack Administrations: Challenges and Control
Bibliographic record
Abstract
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.
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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 | 0.027 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".