Bibliographic record
Abstract
Causation in insurance law is an area where courts continuously experience difficulties. This is largely because in insurance law causation is used as a payouttrigger, a separate and distinct element from the traditional “but for” causation generally found in tort. This article proposes a framework for understanding the mechanics of causation as a payout trigger. This is done largely through a focus on the resulting loss and how it occurred. This framework also provides an opportunity to parse through the problems associated with concurrent causation (for example, when loss is caused by both smoke and fire after a lightning strike). Concurrent causation must be analyzed using a liberal approach derived from the Derksen case. The framework makes use of a temporal analysis to determine the relevance of a cause, working backward from the loss. In the final stages of the analysis, thelanguage used in the insurance policy must be interpreted using a purposive approach by considering drafting intent, as well as the consequences of coverage and its associated gaps. The article aims to streamline insurance causation analysis in order to promote more consistent and holistic results in insurance coverage disputes.
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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".