Fortuity Clauses in Liability Insurance: Solving Coverage Dilemmas for Intentional and Criminal Conduct
Bibliographic record
Abstract
Should losses resulting from criminal or intentional conduct be insurable through liability insurance? Insurers have crafted fortuity clauses in liability policies in order to ensure that coverage is available only for fortuitous losses, not certainties. Two common fortuity clauses oust coverage for losses arising from “intentional” or “criminal” acts. Yet what is “intentional” conduct? And what is “criminal” conduct? These interpretive problems which have vexed Canadian courts for decades have produced jumbled insurance jurisprudence which has spawned multiple, distinct ways of answering what appear to be simple insurance coverage questions. The reason courts have had such difficulty with interpreting these particular fortuity clauses is because courts and litigants are often distractingly entranced by the normative pull of morality embedded in the act of excluding liability insurance indemnity coverage for criminal and intentional conduct. The implicit (and sometimes explicit) narrative of fortuity driving these insurance cases inappropriately shifts to a narrative about punishment, deterrence, and morality. This article explores that shift and provides a new interpretive framework which restores a principled approach to interpreting these fortuity clauses. It does so by grounding courts’ and litigants’ thinking in the notion that these clauses are there to respond to moral hazard fortuity concerns within the context of a publicly regulated accident compensation system of which insurance is a fundamental part.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.047 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".