Bibliographic record
Abstract
The scope of the defence of illegality in the law of tort is often controversial and difficult. This paper examines the issues in the specific context of negligence claims for personal injury suffered by a passenger against a driver with whom he was engaged in an illegal venture. This problem has been considered in a recent decision of the High Court of Australia and two decisions of the Court of Appeal in England. These cases are analyzed and several different approaches to the application of the illegality defence are identified. The proposition that it is impossible to identify the driver’s standard of care in such cases is rejected. The notion that the scope of the defence of illegality can be determined by considering whether to decline to apply it would lead to ‘incongruity’ within the law has attracted support in Australia and Canada. Nevertheless the paper argues that the apparent utility of the test as a relatively straightforward means of determining when claims should fail is deceptive, and that it is unworkable in cases of any complexity. The proposition that causation can provide a necessary but not sufficient condition for the application of the illegality defence in the type of case under consideration, however, is welcomed. But it is also concluded that some reference to intuitive perceptions as to the gravity of the parties’ illegal adventure will often be inescapable in order to avoid applying the defence to less serious cases in which its application would be unjust.
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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".