The Elusive Third Way? The Role of Positive Morality in <i>Recognizing Wrongs</i>
Bibliographic record
Abstract
Tort theory is deeply divided between consequentialist economic theories,1 on the one hand, and corrective-justice or rights-based theories,2 on the other.3 A relatively stable narrative has emerged about how “civil recourse theory”4 fits into the picture: as a rights-based account that stands against the consequentialist views, but differs only slightly from the various corrective justice views.5 This narrative misapprehends the aspirations and possibilities of Goldberg and Zipursky’s account. Recognizing Wrongs holds the potential—though yet unfulfilled, in my view—to chart what has been called “the elusive third way” past these entrenched positions.6 Here is my argument in short: Recognizing Wrongs makes moral space for the role of the plaintiff within the structure of tort adjudication by way of the civil recourse principle. This political justification, focusing on the moral rights of individuals,7 gives it a potential normative advantage over consequentialist views. Recognizing Wrongs also gives a convincing account of the nature of the legal obligations in tort,8 which makes it preferable to corrective justice and other fully moralized accounts of tort.9 Each of these moves is distinctive within tort theory.
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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".