Adding mediation to India's corporate resolution process
Bibliographic record
Abstract
Abstract India's new insolvency law, the Insolvency and Bankruptcy Code 2016 (‘IBC’) was introduced to improve the efficiency of the resolution process. Although there is much to be credited in the law, the practice of it has shown that the process is often delayed by excessive litigation. This article aimed to study delays under the IBC by looking at the application of the law and providing an alternative feminist assessment. This assessment highlights that a feminist value missing from the practice of the IBC is the inclusion of stakeholders, and particularly a non‐adversarial system that helps stakeholders manage and resolve conflict amicably (and expeditiously) during insolvency resolution. Litigation before tribunals and courts to adjudicate upon stakeholder conflict often leads to significant delays. The article proposes a model that nudges parties towards mediation during a Corporate Insolvency Resolution Process (‘CIRP’) within the IBC, in order to preserve relationships and reduce delays.
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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 | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 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".