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Record W6907199400 · doi:10.20381/ruor-31314

The Regulation of Insurers in Canada as an Area of Law

2025· dissertation· en· W6907199400 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Ottawa - Library · 2025
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicLaw, logistics, and international trade
Canadian institutionsnot available
Fundersnot available
KeywordsPosition (finance)Key person insuranceControl (management)Insurance lawPrudential regulationInsurance policyCasualty insuranceGeneral insurance

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.179
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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