The priorities dilemma in the <scp>EU</scp> preventive restructuring directive: Absolute or relative priority rule?
Bibliographic record
Abstract
Abstract The EU Directive on Preventive Restructuring Frameworks gives the EU Member States (“MSs”) the choice between implementing two fairness rules in cross‐class cram‐down: the US‐style absolute priority rule (“APR”) or the newly conceived relative priority rule (“RPR”). This article argues that there is no good reason for the MSs to implement the RPR in domestic law. While the APR effectively protects the rights of the dissenting classes to get what they are entitled to, the RPR increases moral hazard and opportunism. Also, it might make debt investments in the EU unattractive. On top of that, this article shows that the RPR lacks a clear theoretical justification. One of the main reasons why the RPR was introduced in the Directive alongside the APR is that the RPR was thought to provide a solution to some of the APR's problems. This article considers three of those problems (i.e., the “valuation problem”, the “hold‐out problem” and the “problem of the relevant shareholders”) and explains the reasons why the RPR is not an appropriate solution for these. Among these three problems, the most troublesome one, from the perspective of the EU, is that the APR makes it difficult to award value to the equity of SMEs (the “problem of the relevant shareholders”). This article argues that using the RPR to deal with this problem would incentivize the shareholders to behave opportunistically and to orchestrate the restructuring. Instead of the RPR, this article suggests two alternative techniques which MSs can enact to better address the issue: the new value exception “in kind” and the disposable income method.
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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 | 0.029 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".