Harmonising insolvency law in the <scp>EU</scp>: New thoughts on old ideas in the wake of the <scp>COVID</scp>‐19 pandemic
Bibliographic record
Abstract
Abstract While the harmonisation of insolvency law in the European Union (EU) has been a top priority on the European institutions' agenda in the last decade, it is well known that this endeavour has been slow and has often met resistance from the Member States. The COVID‐19 pandemic revealed that top‐down harmonisation of insolvency (i.e., introduced at EU level) has been temporarily halted. The urgency to control or mitigate the economically and financially destructive effects of the pandemic has, nevertheless, forced European governments to adopt domestic strategies and laws in the area of insolvency. Interestingly, however, such measures show that insolvency and restructuring law responses to the COVID‐19 pandemic, albeit largely uncoordinated, reflect a phenomenon of bottom‐up harmonisation (i.e., introduced by Member States) indicating a convergence towards common approaches. This paper interrogates the insolvency law responses to the COVID‐19 pandemic in six European countries (Denmark, France, Germany, Italy, The Netherlands, the United Kingdom). It uncovers the inadequacy of the EU's harmonisation language, and the limits of harmonisation strategies in insolvency and restructuring law. Finally, it promotes the formulation of a wider‐encompassing definition of “legal harmonisation”.
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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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".