Court Disclosures of Firms in Chapter 11 Bankruptcy
Bibliographic record
Abstract
ABSTRACT Stakeholders in the Chapter 11 reorganization process face significant information uncertainty about the post‐emergence prospects of the firm. The U.S. Bankruptcy Code requires a debtor to provide a disclosure statement containing “adequate information” about its financial status and a proposed reorganization plan but stops short of rigidly defining the adequacy standard. We document the heterogeneity in disclosure statement information across 16 distinct attributes and examine the variation in disclosures along several dimensions that reflect agency costs and coordination problems. We observe that Chapter 11 disclosure correlates more with claimant‐ and case‐specific characteristics than pre‐bankruptcy debtor characteristics. Our results illustrate the importance of institutional features in specific disclosure settings such as bankruptcy court filings. The research questions and methods of this study were registered via the Journal of Accounting Research ’s registration‐based editorial process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".