Bibliographic record
Abstract
The following realization has begun to dominate contemporary tort theory: in order to understand tort law, theorists must also focus on the legal power that tort law vests in tort victims to pursue a remedy, not only on the implications of holding tortfeasors liable for such a remedy. This insight has lead some of the leading theorists of tort law – often writing under the banner of ‘civil recourse theory’ – to suggest that tort law empowers tort victims to pursue and even to obtain redress from tortfeasors. This view has even been expanded to describe private law in general. Yet, close scrutiny reveals that tort law mostly does not vest in tort victims a legal power over the rights of tortfeasors. The same is most likely true for private law more broadly. For the sake both of descriptive accuracy and of realizing its prescriptive potential, civil recourse theory is best amended to view the legal rights and powers of tort victims, as well as the realities of civil litigation, more soberly and with more conceptual accuracy. This article endorses and grounds the more modest – and I think, orthodox – view on how tort law and private law more broadly empower victims of civil wrongs. Adopting this view makes civil recourse theory truer, yet less novel.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".