Bibliographic record
Abstract
This chapter examines vicarious liability, that is, liability in tort for the tortious acts of others, and the insights that may be gained from a comparative law perspective. John Bell’s work on vicarious liability was cited by the UK Supreme Court in Cox v. Ministry of Justice [2016] UKSC 10, in support of its view that vicarious liability could be justified where the defendants had, in the exercise of their mission or enterprise, placed the tortfeasor in a position where the commission of the tort was an inherent risk of the activities assigned to them. This chapter examines comparatively the role ’risk’ plays in contemporary vicarious liability discourse. While civil law systems such as France have long embraced notions of risk, the common law courts have been slower to respond. Since 2000, however, UK and Canadian case law has indicated a willingness to move in this direction. In contrast, the Australian courts continue to resist the lure of risk- based analysis as an explanation and justification for the imposition of vicarious liability. Is relying on risk as a justification a ’risky business’ or inevitable if one wishes to expand this doctrine to meet the needs of meritorious litigants?
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".