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Record W1996343216 · doi:10.7202/1018985ar

Privacy, Corrective Justice, and Incrementalism: Legal Imagination and the Recognition of a Privacy Tort in Ontario

2013· article· en· W1996343216 on OpenAlexvenueaboutno aff
Thomas D.C. Bennett

Bibliographic record

VenueMcGill Law Journal · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsTortLawEconomic JusticeIncrementalismAppealPolitical scienceRelevance (law)Privacy lawPrivacy laws of the United StatesRight to privacySeclusionSociologyLaw and economicsInformation privacyPrivacy policyComputer securityComputer science

Abstract

fetched live from OpenAlex

This article considers the nature of common law development as exemplified by the recent privacy case of Jones v. Tsige . The author focuses on Jones , in which the Ontario Court of Appeal recognized the novel privacy tort of “intrusion upon seclusion”. Using a detailed analysis of the case as its basis, the article explores issues which have much wider significance for the judicial development of privacy laws: the process of incremental elaboration of the law, the moral impulses at work within it, and the relevance of imagination to its operations. By drawing out these discrete issues and analyzing the role that each plays in Jones , the article offers a framework for examining such questions in future privacy cases. Moreover, this article argues that the judgment in Jones brings valuable clarity to the analysis of the process of common law development. In particular, the essay concludes that the novel privacy tort recognized in Jones is the result of a legitimate incremental development rather than an instance of undue judicial activism.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.021
GPT teacher head0.267
Teacher spread0.246 · 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 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

Citations2
Published2013
Admission routes2
Has abstractyes

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