An Introduction to Canadian Law of Restitution and Unjust Enrichment
Bibliographic record
Abstract
An Introduction to the Canadian Law of Restitution and Unjust Enrichment is a concise and accessible account of the law of restitution or unjust enrichment, constituting a third branch of the law of obligations of the Canadian common law jurisdictions. It provides a comprehensive account of the restitutionary remedies available in the context of benefits conferred by mistake, under duress and compulsion of various kinds, through performance of contracts unenforceable at law or in equity, in necessitous circumstances, and upon the dissolution of relationships of cohabitation. This publication also describes the law providing restitutionary recovery of benefits acquired through wrongful conduct such as: Breach of fiduciary duty Breach of confidence Waiver of tort It also provides an account of three-party cases, in which a third party confers benefits on the defendant which, in justice, ought to be turned over to the plaintiff. Recent developments increasing the restitutionary liability of public authorities are reviewed. Separate chapters are devoted to the defences to restitutionary claims and the various remedies available, including constructive trust. Attention is also paid to academic controversies concerning the meaning of basic terminology and their implications for the scope, structure, and accessibility of the subject. -- Publisher description.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.007 |
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".