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Record W2603201379

Limiting the Legal Liability of Religious Institutions for their Clergy: Cavanaugh v. Grenville Christian College

2016· article· en· W2603201379 on OpenAlexvenueaboutno aff
M.H. Ogilvie

Bibliographic record

VenueDalhousie law journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMulticultural Socio-Legal Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceAppealLiabilityObligationLawJurisprudenceLegal liability
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this article is to explore the case law relating to the potential legal liability of ecclesiastical institutions for the conduct of their clergy and lay employees in the tort of negligence, vicarious liability and breach of fiduciary duty. While a number of cases have resulted in findings of liability, especially in those relating to the Indian residential schools, a recent decision from the Ontario Court of Appeal, Cavanaugh v. Grenville Christian College, suggests ways of thinking about the limits and scope of liability for institutions whose charitable purposes are occasionally betrayed by rogue persons over whom they may have little control. Cet article a pour objectif d’examiner la jurisprudence sur la responsabilite legale potentielle des institutions ecclesiastiques pour la conduite de leurs membres et de leurs employes laics dans les delits de negligence, dans les affaires de responsabilite du fait d’autrui et de manquement a l’obligation fiduciaire. Quoique de nombreux jugements, en particulier ceux qui ont trait aux pensionnats autochtones, ont conclu a la responsabilite des institutions, une decision recente de la Cour d’appel de l’Ontario, Cavanaugh v. Grenville Christian College, suggere des pistes de reflexion sur l’ampleur et les limites de la responsabilite pour les institutions dont les objectifs caritatifs sont parfois trahis par des delinquants sur qui elles peuvent n’avoir que peu de controle.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.002
Scholarly communication0.0000.000
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 head0.040
GPT teacher head0.304
Teacher spread0.264 · 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.

Study designNot applicable
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
Published2016
Admission routes2
Has abstractyes

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