Auto Insurance as Social Contract: Solving Automobile Insurance Coverage Disputes Through a Public Regulatory Framework
Bibliographic record
Abstract
Automobile insurance in Canada is a product with a decidedly public purpose, a social contract. The provincial governments are heavily involved in the creation, regulation, drafting, and operation of the automobile insurance regime in any particular province. This public flavour to Canadian automobile insurance necessarily should affect the way one assesses the availability of insurance coverage in accident situations involving injuries or death. Understanding the limits of automobile insurance coverage for injuries or death in any given accident situation in Canada should be an exercise of interpretation akin to discerning the meaning of a public regulatory instrument with a public purpose, like a statute. This article proposes a novel interpretive framework for Canadian automobile insurance coverage disputes, one which accounts for the public purpose of such insurance and which searches for the true intent behind the language in the coveragegranting instruments. The framework also prompts an assessment of coverage decision consequences in a public compensatory regime and, in instances of coverage ambiguity, solves those ambiguities through basic tools of consumer protection.
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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".