Debt restructuring, By RodrigoOlivares‐Caminal, RandallGuynn, AlanKornberg, EricMcLaughlin, SarahPaterson and DalvinderSingh (3rd edition) (2022, OUP, New York). lxviii and 809 pages, £250.00, ISBN: 978‐019‐28481‐0‐9
Bibliographic record
Abstract
Debt restructuring is a complex business, sometimes under the framework of insolvency law and sometimes purely under contractual terms negotiated on a case-by-case basis by affected parties.Different from many books focusing on corporate debt reorganisation, this book is perhaps the first one covering debt rehabilitation of three types of debtors: ordinary businesses, financial institutions and sovereigns.Meanwhile, given the eminence of New York and London as the two major global debt restructuring centres, this book sheds much light on how the legislative and judicial infrastructures of these two cities support debt restructuring efforts.The insights presented in this book apparently result from the strong team of six authors, three of them (Professors Caminal, Paterson and Singh) being internationally recognised scholars, while the others (Guynn, Kornberg and McLaughlin) are top practitioners involved in many highprofile restructuring cases not only in New York and London but also globally.This book is well researched, with major case law cited, so as to ensure the reader is better informed.For example, when examining the powers conferred on the USA Federal Banking Resolution Authority, Professor Singh uses Colins v Yellen to elaborate on the details of resolution tools offered by US lawmakers, helping the reader to thoroughly appreciate the difference between the law on the books and the law in action.Apart from the research excellence of the three leading academic authors, the real-world guidance offered by the three outstanding practitioner authors is also highly recommendable.For example, in analysing the challenges of out-of-court work-outs in the United States, Kornberg addresses one significant legal technique in promoting a debt restructuring plan: targeting substantial creditors only; such advice is so valuable, since in reality for an out-of-court debt restructuring, seeking unanimous consent from all creditors is virtually impossible and is also unnecessary, while a debt restructuring effort can still succeed with the agreement of major consenting but substantial creditors, even if all small-claim creditors are not touched and are fully paid.The author generously shares his experience, offering much needed professional instruction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.020 |
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".