Interceptive Subtraction, Unjust Enrichment and Wrongs—A Reply to Professor Birks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An Introduction to the Law of Restitution was a landmark in private law. More clearly than any preceding work, it unpacked the ambiguity inherent in the notion of “the claimant's expense by delineating two forms of “unjust enrichment. (1) The autonomous action in unjust enrichment involves a subtractive expense . The defendant acquires a benefit from the claimant in circumstances that the law regards as reversible. The response is always restitution . The defendant must give the enrichment, or its value, back to the claimant. (2) Unjust enrichment by wrongdoing, in contrast, is concerned with a normative or wrongful expense . The defendant acquires a benefit, usually from a third party, as a result of breaching an obligation owed to the claimant ( e.g. trespass to land). Although the standard response to a civil wrong is compensation for the claimant's loss, a court exceptionally may compel the defendant to give up, or disgorge, his ill-gotten gain.
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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 | 0.001 | 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 | 0.002 | 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) | 0.000 | 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 it