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Record W7162000238 · doi:10.82308/50162

The necessity of a broadened, cost-effective, and widely recognized common law invasion of privacy tort in technocentric Canada

2016· dissertation· en· W7162000238 on OpenAlexaboutno aff
Michael Mantle

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicLaw, Rights, and Freedoms
Canadian institutionsnot available
Fundersnot available
KeywordsTortCommon lawPrivacy laws of the United StatesInformation privacyPrivacy lawPrivacy by DesignAppealLegislaturePrivacy policyPersonally identifiable information

Abstract

fetched live from OpenAlex

There is a strong causal relationship between the rapid advances in technology and thedevaluation of personal privacy as an intangible commodity. Considering this, common law provinces in technocentric Canada are in need of a potent, cost-effective, and widely recognized invasion of privacy tort.While a veritable jungle of laws exist that provide for significant privacy protection in thecriminal, constitutional, quasi-constitutional, and a host of other realms, relatively little attentionhas been paid to the civil sphere. The result of this oversight is a lackluster system of civilredress (specifically in common law provinces) against citizens who, either negligently or intentionally, invade the privacy of other citizens.Beginning with the conception of privacy itself, this work will explore the myriad ofdifficulties that have been associated with defining this historically elusive term. Additionally, byborrowing directly from Charter jurisprudence, criminal cases, and academic literature, thiswork will put forth an amalgamated approach to privacy that should be utilized in the contextof a robust common law invasion of privacy tort in Canada. The reader will be able to juxtaposethis approach to the other, arguably weaker, methods that Canadian jurisdictions have utilized toaddress privacy invasions in the civil sphere. The weaknesses of the legislative enactments, theuse of the traditional or good enough torts, and most recently, the intrusion upon seclusion causeof action established by the Ontario Court of Appeal in Jones v Tsige will be explored in depth.Finally, considering the definition of privacy suggested by the author and the previously utilizedlegal mechanisms for protecting privacy, a potential approach to a (mostly) Canadian made tortfor invasion of privacy will be espoused.

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 categoriesnone
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 score0.365
Threshold uncertainty score0.667

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 studies0.0000.002
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 head0.012
GPT teacher head0.278
Teacher spread0.266 · 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
Published2016
Admission routes1
Has abstractyes

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