The evolution of credit bidding : its recent journey and logical next step
Bibliographic record
Abstract
Credit bidding is a US construct that enables secured creditors to use their secured claims, instead of having to raise additional capital, to bid on their collateral at an asset sale. The US legislature amended the bankruptcy statutes to include credit bidding specifically to prevent the undervaluation of collateral. Recent US case law has re-evaluated when secured creditors are entitled to credit bid and when debtors might be able to deny this right through the use of a loophole subsection. This subsection allows a debtor to deny secured creditors the right to credit bid if the debtor can satisfy their claims by providing an “indubitable equivalent.” While the US Supreme Court ultimately determined that the indubitable equivalent subsection cannot be used to deny secured creditors the right to credit bid at an asset sale, the case law adeptly highlights the merits of credit bidding while demonstrating the dangers of specific legislation. Although Canada does not have legislation regarding credit bidding, it has nonetheless been incorporated into Canadian insolvency proceedings through cross-border cases. This thesis discusses both the benefits and issues involved with credit bidding in a US and Canadian context, reviewing relevant case law and legislation in both jurisdictions. It also discusses the current status of credit bidding in Canada, which, without specific legislation to state otherwise, current case law has found to be permissible but not a right. Consequently, this thesis proposes that credit bidding should be added to Canadian insolvency legislation.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".