Energising courts to continue breaking new ground in insolvency and restructuring cases
Bibliographic record
Abstract
Abstract The role of judges in restructuring and insolvency proceedings has been of particular interest to the EU legislator in recent years. It is in matters of insolvency and restructuring that a court and its judges have to fulfil a set of five criteria: (a) a general understanding of business management (so as not to assume managerial tasks), (b) understanding what is needed to effectively enforce the rights of both secured and unsecured creditors outside of insolvency proceedings (as, for instance, a stay may influence pre‐insolvency enforcement rights), (c), preferably, be a specialist in commercial matters, (d) be impartial and independent, and (e) where practical, have specialised insolvency expertise. Where businesses are operating across borders, the latter criterion includes cross‐border knowhow.During the 20th century, several steps have been developed with the aim of improving the environment within which these criteria can be met. Courts themselves may wish to improve their level of quality and effectiveness. In this contribution, several examples are discussed that have been put in place or will emerge soon that can enhance the court's professional standing and its performance, in (international) insolvency and restructuring, as a “good” judge. These developments may assist Member States in improving their judicial frameworks, as well as (associations of) members of courts or judicial and administrative authorities dealing with procedures concerning insolvency and restructuring in developing the necessary expertise for their responsibilities as formulated in the EU Preventive Restructuring Directive. Moreover, the duty of cross‐border cooperation between courts in EU law underpins the discussion and the importance of cooperation as is underscored by the adoption of this Directive.
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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 | 0.046 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.020 | 0.018 |
| Scholarly communication | 0.032 | 0.013 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.026 | 0.019 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".