Bibliographic record
Abstract
Sometimes a benefit is derived from a legal wrong that does not cause – or that does not appear to cause – any corresponding loss. The question whether such benefits must be given up, and if so for what reason, has caused much conceptual agonizing; it is an issue that has been found impossible to classify, cutting across the legal categories of contract, tort, property, and unjust enrichment, and often involving general considerations of public policy. An example is the well-known Kentucky case of Edwards v. Lee's Administrator (1936), where the defendant profited by admitting tourists to see a spectacular cave that was partly underneath the plaintiff's land, though only accessible from the defendant's. The court held that the plaintiff, though not himself in a position to profit from the cave, was entitled to recover a reasonable portion of the defendant's profits. Similarly, a person might use a corner of his neighbour's land without permission for access to a building site, saving himself substantial construction costs, but doing no damage to the land. Or, to take an older example, the plaintiff keeps horses for hire, and the defendant takes one out without permission, and brings it back unharmed. Can he say, in answer to a claim for money, ‘Against what loss do you want to be restored? I restore the horse. There is no loss. The horse is none the worse; it is the better for the exercise’?
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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".