Bibliographic record
Abstract
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.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".