Fiduciary Law and the Constructive Trust: Perfecting the Fiduciary Undertaking
Bibliographic record
Abstract
Two recent unanimous decisions from the Supreme Court of Canada in Galambos v Perez and Alberta v Elder Advocates of Alberta Society have narrowed and refreshed the requirements for recognizing fiduciary relationships and obligations. All fiduciary obligations must be founded by an undertaking, either express or implied, on the part of the fiduciary to act in the best interest of the beneficiary. At the heart of the fiduciary obligation, the undertaking of a fiduciary may also serve as a foundation for the goals of fiduciary accountability. The developing “Galambos approach” remains incomplete in its application in this regard. In the spirit of Galambos and Elder Advocates, I propose that the undertaking of the fiduciary can provide principled guidance in the availability of gain-based relief for breach of fiduciary duty. Particularly, I suggest that the imposition of a constructive trust as proprietary gain-based relief may be rationalized under the objective of perfecting or enforcing the fiduciary undertaking. To demonstrate my proposal, I investigate three example undertakings and breaches of fiduciary duty in which the fiduciary acquires property through the breach of duty. By grounding this overall discussion towards a conceptual remedial goal of enforcing the fiduciary’s undertaking, Galambos may spark the development of a principled approach to understanding both the making and the breach of fiduciary obligations.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.082 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".