MODEL OF PROCEDURAL COORDINATION IN CASE OF INSOLVENCY (BANKRUPTCY) OF AN ENTERPRISE GROUP
Bibliographic record
Abstract
Abstract: there are no rules on the insolvency of erprise groups in Russian bankruptcy law. Traditional and generally accepted is the principle of “one debtor – one procedure”, which corresponds to the principle of isolation and autonomy of each legal entity. This approach does not correspond to the current economic environment, in which there are a large number of interconnected companies that form enterprise groups. This leads to an increasing number of problems in the bankruptcy sector, namely, to an increase in the number of insolvency cases of legal entities with a constant decrease in the percentage of satisfaction of creditors’ claims. In this regard, opinions are expressed about the need to introduce the regime of bankruptcy of enterprise group into Russian legislation. The purpose of this article is to study the enterprise group insolvency model, namely the procedural coordination model. The article provides a comprehensive analysis of procedural coordination and conducts a comparative legal study of those legislations in which this model is already used. The conclusion is made about the expediency of using the procedural coordination model instead of the substantive consolidation model and the need to introduce into the Russian bankruptcy legislation rules that fix the insolvency regime of debtors – members of an enterprise group.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".