Reorganizations, Sales, and the Changing Face of Restructuring in Canada: Quantitative Outcomes of 2012 and 2013 CCAA Proceedings
Bibliographic record
Abstract
This article examines quantitative data on the outcomes of proceedings under the Companies’ Creditors Arrangement Act (CCAA), Canada’s principal statute for resolving large, complex corporate insolvencies. In particular, this article compares the durations, direct costs, and returns to different classes of creditors generated by traditional reorganizations under the CCAA and by “liquidating CCAAs”—that is, proceedings in which the insolvent debtor sells substantially all of its assets rather than reorganizing itself. The article makes a number of contributions to the existing scholarship. Firstly, quantitative data on CCAA proceedings are rare. The data examined here, collected by the author from proceedings initiated in 2012 and 2013, provide novel insights into the functioning of the CCAA. Secondly, the article questions much of the conventional wisdom surrounding liquidating CCAAs based upon the data, suggesting that further study is required. Thirdly, the article proposes reforms to address the problems raised by liquidating CCAAs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".