An Oppression Remedy v Fraudulent Conveyance Legislation: Which Legislative Scheme Better Protects Creditors from Opportunistic Debtors?
Bibliographic record
Abstract
Canada’s regime governing fraudulent conveyances is deficient. It is based on legislation enacted in the sixteenth century; it focuses on the debtor’s intention; it is criminal in origin; and it has generated confusing, unpredictable and inconsistent case law. Overall, it does not offer creditors much protection. But this is not a novel critique. The regime has been the subject of endless criticism over the decades, all of which culminated in proposed legislation to replace it. In 2012, the Uniform Law Conference of Canada recommended the adoption of the Uniform Reviewable Transactions Act to replace our current fraudulent conveyance and preferences legislation. However, the proposed legislation is not the only solution; other legislation could also replace current fraudulent conveyance laws. This paper argues that legislation like the oppression remedy, but applicable to individual debtors as well as corporations, should take the place of fraudulent conveyance laws. The structure of this remedy would better address improper transfers by debtors. Intention-based legislation is problematic. It attempts to achieve an effects-based purpose by applying an intention-based test. An effects-based test is also not the answer, as it would be too broad. It would protect creditors but would also unduly restrict debtors from carrying out legitimate transactions. The answer, rather, is to replace the current intention-based provisions with an oppression remedy, as in, an equitable effects-based test which requires not just prejudice, but unfair prejudice. The requirement of unfairness provides a notable limitation: a remedy is contingent on whether the impugned behaviour is consistent with the parties’ reasonable expectations when they entered into the contract. By determining creditors’ reasonable expectations when they entered into the transaction, the legislation protects both creditors and debtors. It reverses transactions the creditors would not have reasonably expected when they entered into the lending agreement, and in so doing, it prevents debtors from moving assets out of creditors’ reach while also allowing them to take risks, possibly even gamble with the company’s money. In other words, the remedy protects creditors while also preventing them from benefiting ex-post, from a failed transaction they agreed to ex-ante.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".