Bibliographic record
Abstract
The constructive trust is a court order declaring that the defendant holds a disputed asset on trust for the plaintiff. The aim of this paper is to develop a theory of the constructive trust based on economic considerations. It is commonly said that the constructive trust serves two functions: (1) a deterrence function (the prevention of unconscionable conduct); and (2) a restitutionary function (the reversal of unjust enrichment). This taxonomy overlooks the constructive trust's perfectionary function, namely the enforcement of express and implied bargains. Some constructive trusts serve an explicitly perfectionary function: the constructive trust to perfect an agreement to transfer is a case in point. Other constructive trusts appear to serve a deterrence or restitutionary function. However, on closer examination these constructive trusts turn out to be perfectionary as well. The paper discusses five leading Australian, Canadian and English cases, concluding that in each case the primary objective in granting or withholding the remedy is to reproduce the outcome the parties are likely to have agreed on up front if bargaining between them had been costless. Express or implicit cost benefit analysis is an indisensable part of the decision-making process.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".