Calculating Judgment Interest After Bank of America Canada v. Mutual Trust Co.: Difficulties Compounded
Bibliographic record
Abstract
Until recently, compound judgment interest was only available in rare circumstances, namely where courts exercised their equitable jurisdiction. Simple interest was the ordinary measure awarded by the courts. However, in Bank of America Canada v. Mutual Trust Co. the Supreme Court of Canada stunned Canadaʼs legal theorists and practitioners by holding that compound judgment interest could be awarded at common law. It was the firm belief of Major J. that such relief was necessary to protect the full time value of money owed to judgment creditors. This paper considers the hidden implications of the decision. Attention is given to the Supreme Court of Canada’s ambiguous use of restitution principles in awarding compound interest. The context of the case suggests that the Court was possibly referring to disgorgement, a type of restitution. However, neither the terminology used, nor the circumstances of the case clarify whether this was the Courtʼs true intention. Additionally, Major J. decided to limit the use of restitution principles in cases of contract breach (where it traditionally was unavailable) to those involving inefficient breaches. The uncertainly with regard to the extent of this limit is examined. Finally, consideration is given to the possibility of contract rights attaining the status of property rights through the Court’s apparent use of disgorgement principles.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.009 |
| 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".