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Record W2310202944

A Common Law Tort of Privacy? The Challenges of Developing a Human Rights Tort

2015· article· en· W2310202944 on OpenAlexaboutno aff
Paula Giliker

Bibliographic record

VenueBristol Research (University of Bristol) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsTortCommon lawLawJurisdictionThe Right to PrivacyPolitical sciencePrivacy laws of the United StatesInformation privacy lawPrivacy lawData Protection Act 1998Information privacyHuman rightsBusinessPrivacy policy
DOInot available

Abstract

fetched live from OpenAlex

This article will examine the evolution of a new tort – that of misuse of private information – in the courts of England and Wales. Stimulated by the introduction of the UK Human Rights Act 1998 (c 42), the English courts are moving towards recognition of a distinct tort which is capable of responding to advances in technology which give rise to increased possibilities for intrusion into the personal lives of private individuals. While such a development may seem preferable to the previous practice of “shoehorning” claims into the existing action for breach of confidence, this article will consider, with reference to recent case law in New Zealand and the Canadian province of Ontario, the challenges which recognition of torts protecting privacy rights present to traditional common law reasoning. In particular, it will examine the extent to which the constitutional framework in each jurisdiction, which provides for protection of a right to privacy and freedom of expression, has led to different responses. Developing a privacy tort is no easy task, both in defining the interest protected and determining its scope and appropriate remedial framework. In analysing how the courts have addressed these issues, the article will consider whether incremental case law development or legislative intervention is more likely to lead to the coherent evolution of this area of law.

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)0.002
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 score0.932
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.233
GPT teacher head0.404
Teacher spread0.170 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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