Bibliographic record
Abstract
Abstract Hybrid restructuring procedures, such as pre‐packs, have been encouraged to deal with an expected increase in insolvent firms in the aftermath of the COVID‐19 pandemic. Pre‐packs have been gaining popularity as a restructuring mechanism across the world. India too introduced a pre‐packaged insolvency resolution process for micro, small and medium enterprises (MSMEs) in April 2021. This article first provides a brief overview of the pre‐pack models employed in certain other jurisdictions such as the United States of America, United Kingdom and Singapore with a view to understanding the key features of pre‐pack models and how they have been operationalised in these jurisdictions. It then briefly discusses other restructuring avenues, that were available to a corporate debtor in India, and the circumstances that led up to the introduction of pre‐packs in India. Finally, it provides a detailed overview of the Indian pre‐pack regime and evaluates its effectiveness for MSMEs, potential issues that may cause delays in the process and how the current pre‐pack regime can be further streamlined. It argues that some of the procedural requirements and features of the Indian pre‐pack regime may not be suitable for MSME insolvencies.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".