Assuring the Future: Reform of the Insolvency Framework for Insurance Companies and Other Financial Institutions Under the Canadian Winding-Up and Restructuring Act
Bibliographic record
Abstract
The purpose of this article is to examine the strengths and weakness of the Winding-Up and Restructuring Act (WURA), and provide baseline information to generate a much needed policy discussion regarding its future. This lengthy statute has been amended piecemeal over its 100 year history, and it is overdue for legislative reform to meet current challenges for financially distressed banks, insurance companies and other financial institutions. While there are a myriad of issues which could potentially be addressed, this article focuses on five critically important issues for which reform should be considered: stakeholder rights, asset realization, restructuring, governance, and cross-border insolvency. There is currently no ability to effectively restructure insurance companies and other financial institutions under the WURA; and the Companies' Creditors Arrangement Act (CCAA) does not apply to financial institutions. This article suggests that the WURA should be reformed to provide a viable restructuring scheme that takes account of the special nature of financial intuitions. The article suggests that liquidators should be authorized to undertake restructuring where it is in the best interests of the stakeholders. The article also advocates increasing protection for policyholders and employees, recognizing the special nature of financial firm failure.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".