Bibliographic record
Abstract
In Moses v Macferlan, Lord Mansfield used money had and received, a common law money count, to provide relief in a case where an action’s outcome failed to align with the actor’s intention. In the First Restatement of Restitution, Warren Seavey and Austin Scott gathered together all cases, quasi-contractual and equitable, under the single principle that ‘a person who has been unjustly enriched at the expense of another is required to make restitution to the other.’ These two influential acts of fusion between common law and equity have caused a great deal of confusion in the scholarship and jurisprudence on unjust enrichment. With the fundamental differences between quasi-contract and equitable unjust enrichment obscured, scholars and judges have struggled to find the single principle or core case that unifies liability in what is now called the law of unjust enrichment. I argue that we can resolve the puzzles of unjust enrichment by rejecting the fusionist claims that started them all – that is, by distinguishing cases of quasi-contract (the common law money counts) from cases of equitable unjust enrichment – and by recognizing that each has a distinctive normative foundation. Quasi-contract, like other common law doctrines, is grounded in respect for the freedom and equality of agents conceived as beings with the capacity for free choice. Quasi-contract is concerned with the objective significance of external acts like requests and agreements on terms; it is not concerned with the frustration of the plaintiff’s particular purpose in acting. Equitable unjust enrichment, like other equitable doctrines that attend to mistakes, expectations, and intentions, is grounded in concern for individual autonomy. It recognizes that a court, as a public institution attuned to law’s self-imposability, cannot enforce an alienation of property with indifference to the way in which it may fail as an expression of the individual’s purposes and reasons for action.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".