Business Rescue in Insolvency Law in Europe: Introducing the ELI Business Rescue Report
Bibliographic record
Abstract
Abstract In a European study, written under the auspices of the European Law Institute, the authors have designed elements of a legal framework that will enable the further development of coherent and functional rules for business rescue in Europe. Based on the recommendations of international organisations, such as UNCITRAL and the World Bank, as well as the insolvency laws of EU Member States, comparative research has led to a lengthy report of 10 chapters and more than 100 recommendations which are described in this article. They range from the need for professional and honest parties involved in the process (insolvency practitioners, turnaround managers, courts and company directors) to the evaluation of specific tools (such as a stay on enforcement actions of creditors and forms of available finance) and procedural safeguards to enable serious rescue efforts of viable businesses, while protecting justified interests. Copyright © 2018 INSOL International and John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".