The Regulation of Insurers in Canada as an Area of Law
Bibliographic record
Abstract
The regulation of insurers in Canada is a subject that has received very little academic attention, perhaps due to a combination of lack of interest and the complex and tedious challenges it poses. Nonetheless, it is a vitally important area. Governments in Canada regulate insurers, including their insurance products, given their very important roles and the position of power that insurers have vis-à-vis their customers. As a result, this regulation seeks two key objectives: to protect the financial rights and interests of insurance customers and certain other stakeholders, and to contribute to public confidence in the insurance sector and the financial system as a whole. Governments use the following two key means to pursue these objectives: a) prudential regulation, which incites insurers to prudently identify, control and manage the various risks they face, and thereby provides some assurances that insurers will be able to live up to their financial obligations; and, b) market conduct regulation, which, in regards to insurance products, incites insurers to treat their customers fairly throughout the lifecycle of these products. This paper will first explore the fundamentals necessary to examine these two types of regulation, including how they compare to insurance law. The paper will then examine the aims of, and rationale for, these types of regulation, and then expose the related regulators. Lastly, it will reveal the means by which these regulators regulate and supervise insurers within their perimeter.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".