Bibliographic record
Abstract
The recent litigation that ended in the House of Lords’ decision in Ashley v. Chief Constable of Sussex Police has brought the intentional torts back to the focus of judicial attention. Most commentary on this decision has focused on a few dicta that purport to support a reading the decision as concerned with private vindication of rights. In this article I examine this decision against a broad shift that has been taking place within tort law, and in particular the tort of negligence, away from ‘private law’ concern with the particular individuals involved in the litigation and towards broader ‘public’ concerns. After describing this shift in the case of negligence, I consider three possible private law interpretations of the role of the intentional torts. I argue that they are all deficient. I then highlight an aspect of Ashley that has been ignored by other commentators and which fits the public interpretation of tort law. I argue that this aspect provides a more convincing explanation for the decision, and one that aligns the intentional torts with the rest of tort law.
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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.041 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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".