Fortuity Victims and the Compensation Gap: Re-Envisioning Liability Insurance Coverage for Intentional and Criminal Conduct
Bibliographic record
Abstract
Insurance is based on the notion that only uncertain, or fortuitous, losses are insurable. There are systemic problems, however, with the consistency in which fortuity clauses are applied in the liability insurance context. Differing interpretive approaches and litigation distortions include the use of at least three interpretive perspectives and two substantive requirements to interpret the intentional act fortuity clause, and four interpretive perspectives to interpret the criminal act fortuity clause. These problems stem from the tension between the two purposes of liability insurance (wealth protection and victim compensation) coupled with a move from explanatory rhetoric about fortuity to explanatory rhetoric about morality.\nThis Article outlines the importance of balancing that tension and examines the problematic effects of these two ubiquitous fortuity clauses that remove coverage for policyholders and simultaneously deny access to compensatory funds for injured victims. The Article argues that intentional and criminal act fortuity clauses need to be more consistently interpreted to avoid a host of inefficient distortion effects that otherwise result from the introduction of moral concerns, and it concludes by offering possible solutions for redress for those accident victims that would still be left, though more predictably, in the liability insurance compensation gap.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| 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".