Bibliographic record
Abstract
The identity and existence of a loss-based defence in the law of unjust enrichment is disputed. Widely known as ‘passing on’, but better identified as ‘disimpoverishment’, this defence has generated confusion and disagreement across and within England, Australia, Canada and the United States of America. This book seeks to address these problems in three ways. First, by providing a solution to the defence’s terminological problems and presenting a coherent picture of the current state of the law. Secondly, by examining whether a defendant’s unjust enrichment can be said to have come ‘at the expense of’ a claimant when a third party has borne the cost of that enrichment. Put another way, whether awards of restitution are, or should be, restricted by the value of a claimant’s loss. And finally, by analyzing the reasons in favour of accepting or rejecting a loss-based defence in the law of unjust enrichment. Numerous scholarly textbooks and law journals have devoted space to these issues. This work, however, has tended to focus narrowly on either particular cases or sets of issues. This book seeks to address this deficiency by collating, and providing total coverage of, the controversies and questions pertaining to a loss-based defence in the law of unjust enrichment.This work will be essential reading for anyone interested in the law of restitution, and in its relationship with other areas of private law.
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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 | 0.009 | 0.019 |
| 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 | 0.046 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".