Bibliographic record
Abstract
Recent years have seen a resurgence of rights-based theories of tort law. These accounts are often presented by their proponents as reflecting the true nature of tort law, which they claim has been corrupted by the infiltration of alien and distorting ideas. Though such ideas have been challenged for the extent to which these faithfully explain contemporary tort law, they have not been examined for their political ideology. I do so in this essay by focusing on two books defending such a rights-based account of tort law. I show that both of them, while purporting to present an account of tort law based purely on observation of legal practice and an analysis of legal concepts, are in fact defending an ideologically right-wing explanation of tort law. After demonstrating how these books defend and criticise various doctrines on the basis of such an ideology, I consider and reject the objection that whatever political bias there may be in these arguments, it is not of the authors’ but rather something found in the law itself. I conclude that whether or not any rights-based version of tort law is desirable, it should not be presented as an account of what tort law is, but rather as part of a political theory about the appropriate relationships between individuals and the state.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".