A Common Law of Tort: Is there a European Rift in the Common Law Family?
Bibliographic record
Abstract
The relationship between English and Commonwealth tort law is well known. In a legal system where the test for remoteness in the torts of negligence and public and private nuisance derives from a dispute in Sydney Harbour and the development of its rules of vicarious liability are strongly influenced by a ruling involving sexual abuse in a Vancouver residential care home, it is difficult to deny the ongoing influence of Commonwealth law on English tort law. And yet the United Kingdom is part of the European Union and thus subject to European Union law. Further, the Human Rights Act 1998, introduced in 2000, requires the English courts to take account of decisions of the European Court of Human rights when relevant to proceedings (section 2). Courts must equally, so far as it is possible to do so, interpret legislation in a way which is compatible with the Convention rights (section 3). A similar interpretative obligation exists under EU law. This paper examines the ongoing relationship between English tort law and its common law family and the extent to which this is changing due to the intrusion of ‘European’ influences from EU and European human rights law. Is there divergence in the common law family, or is this simply part of the natural?
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".