Sales or Plans: A Comparative Account of the “New” Corporate Reorganization
Bibliographic record
Abstract
In this article, Professors Stephanie Ben-Ishai and Stephen Lubben explore the recent surge in popularity of “quick sales”, essentially the prereorganization sale of an insolvent debtor’s assets. In their examination of quick sales, the authors use the recent examples of the General Motors, Chrysler, and Lehman Brothers insolvencies to illustrate the popularity and relevance of preplan sales. The authors then move on to a more detailed discussion of the quick-sales process in the United States and Canada, explaining the differences and similarities between both countries’ regimes, and weighing the costs and benefits of each approach. Ultimately, the authors argue that elements of speed and certainty mark the biggest difference between the two jurisdictions, as the American approach offers greater flexibility, which is apt to facilitate quicker asset sales. However, Ben-Ishai and Lubben assert that the Canadian approach also provides significant benefits, particularly in the realm of employee protection and the ability of the monitor to act as an independent check on quick-sales proceedings. Accordingly, the authors conclude that while the American approach is advantageous in situations with exceptional time constraints, the Canadian approach under the Companies Creditors’ Arrangement Act (CCAA) is more beneficial for a typical corporate reorganization, insofar as the role of the monitor and other limitations of the CCAA prevent overuse of the quick-sales process.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".