Cramdown, reorganization bargaining and inefficient markets: The cases of the <scp>United States</scp> and China
Bibliographic record
Abstract
Abstract The cramdown rule (11 USC s 1,129(b)) originates from the U.S. 1978 Bankruptcy Code as an innovative mechanism. The cramdown provision is rarely used in judicial practice in the United States. However, it is a key rule to understand the parties' bargaining structure. The inactive cramdown provision yet has been widely transplanted into other jurisdictions, including China. Little literature exists to explore the economic rationale of cramdown provision mainly because of its inactiveness in practice. This research explores the economic function of the cramdown provision in an inefficient market, using the cases of United States and China. This research observes that the cramdown provisions can only effectively function with the essential conditions and elements, such as the Best Interests of the Creditors Test, good faith, feasibility, fairness and equitability as well as the absolute priority rule. Therefore, the partial transplantation of the cramdown provision might not be effective.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".