CONTRACTUAL PERFORMANCE, CORRECTIVE JUSTICE, AND DISGORGEMENT FOR BREACH OF CONTRACT
Bibliographic record
Abstract
This paper is about the remedy of disgorgement for breach of contract. In it I argue for two conclusions. I first argue that, prima facie at least, disgorgement damages for breach of contract present something of a puzzle. But second, I argue that if we pay close attention to the notion of contractual performance, this puzzle can be resolved in a way that is consistent with principles of corrective justice. In particular, I suggest that even if a contract gives the promisee a right to only the promisor's performance of the contract, such a right can sometimes entail the acquisition by the promisee of certain rights of ownership. And in situations in which such rights are acquired, the disappointed promisee is entitled to the gains realized by the promisor in breach of contract by reason of the fact that such gains are something to which the promisee has an antecedent right.
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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 | 0.032 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.071 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".