Bibliographic record
Abstract
The rules governing impaired transfers are widely thought to lie at the core of unjust enrichment law. This essay defends two propositions about these rules. First, there is no duty, in the common law, to make restitution of benefits obtained as the result of an impaired transfer (for example, a transfer made by mistake or as a result of fraud or compulsion). Rather than imposing duties to make restitution, or indeed duties of any kind, the rules governing impaired transfers impose only liabilities, in particular liabilities to judicial rulings. The only legal consequence of a mistaken payment is that the recipient is liable to be judicially ordered to repay a sum of money equal to the payment. Second, it matters that the law governing impaired transfers imposes only liabilities, and not duties, because, inter alia, explaining and justifying liabilities is different from explaining and justifying duties. In particular, certain well-known objections to attempts to explain impaired transfer law can be avoided once it is recognized that this law is concerned exclusively with liabilities. In summary, then, this essay argues that the distinction between duty-imposing and liability-imposing rules has important implications for understanding the foundations of the law governing impaired transfers.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".