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Record W20517275 · doi:10.29173/alr98

Personal Responsibility for Intentional Conduct: Protecting the Interests of Innocent Co-Insureds Under Insurance Contracts

2013· article· en· W20517275 on OpenAlexaffvenue
Elizabeth Adjin-Tettey

Bibliographic record

VenueAlberta Law Review · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsWrongdoingStatutory lawDamagesInsurance policyBusinessProperty insuranceFalse Claims ActLaw and economicsLiability insuranceLawCasualty insuranceEconomicsActuarial sciencePolitical science

Abstract

fetched live from OpenAlex

An insured who wilfully damages insured property cannot seek indemnification under an insurance policy because the loss was not a fortuitous one and likely falls within an exclusion clause in the policy. This has historically been referred to as the criminal forfeiture principle, which holds that for public policy reasons a wrongdoer should not be able to benefit from his or her own wrongdoing. The question in situations like this is whether an innocent co-insured should also be barred from recovery for such loss. This article focuses on developments in the law relating to recovery by an innocent co-insured — namely amendments to the British Columbia Insurance Act. The author explores the history of the criminal forfeiture principle and also examines the modern contractual approach to interpreting insurance contracts. This article argues that the modern approach emphasizes property and contract law principles at the expense of protecting the reasonable expectations of an innocent co-insured. The author then examines a key provision in the British Columbia Insurance Act that intends to provide statutory protection for an innocent co-insured. Despite some disadvantages, the author argues that the benefits of the statutory protection outweigh any potential weaknesses.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.153
Threshold uncertainty score0.304

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.012
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.084
GPT teacher head0.394
Teacher spread0.310 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2013
Admission routes2
Has abstractyes

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