Bibliographic record
Abstract
For corrective justice, liability is the consequence of the parties' being correlatively situated as the doer and sufferer of an injustice, and the remedy is seen as undoing that injustice to the extent possible. Combining consideration of legal doctrine and private law theory, this article applies the framework of corrective justice to gain-based damages for torts. Within this framework, restitutionary damages ought to be available only insofar as they correspond to a constituent element in the injustice that the defendant has done to the plaintiff. The radical proposal that allows restitutionary damages for any wrongful gain is unsatisfactory because it fails to link the damages that the plaintiff receives to the normative quality of the defendant's wrong. In contrast, dealings in another's property give rise to such damages because the idea of property includes within the owner's entitlement the potential gainsfrom the property's use or alienation. Restitutionary damages should not be seen as serving a deterrent or punitive function; such a function cannot account for why the plaintiff, of all people, is entitled to the defendant's gain. Properly understood, even situations where the plaintiff's wilfulness or calculation increases the damage award fit within the framework of corrective justice. The corrective justice approach thus repudiates the notion that restitutionary damages are occasions for the promotion of social purposes extrinsic to the juridical relationship between the parties.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| 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".