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Record W2912715773

DIRECT AND VICARIOUS LIABILITY FOR TORT CLAIMS INVOLVING VIOLATION OF PRIVACY

2018· article· en· W2912715773 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Law and Ethics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTortVicarious liabilityStatutory lawStatuteBusinessDuty of careCommon lawContext (archaeology)Strict liabilityLiabilityAccountabilityDeterrence theoryDutyLawLaw and economicsPolitical scienceEconomics
DOInot available

Abstract

fetched live from OpenAlex

The growth of actions for violation of privacy presents a significant risk for defendants and an opportunity for civil claims to provide a mechanism for accountability. However, several key issues that would determine the scope of liability remain unsettled. In most cases, courts have concluded that the existence of statutes dealing with personal information does not exclude the possibility of civil actions, which is important given the limits of statutory remedies. Negligence claims in this context may face issues regarding the duty of care, particularly where the defendant is a public authority, and proof of injury, given that recovery for harms such as stress or economic loss is limited. Therefore, the availability of statutory or common law privacy torts, which do not require proof of actual damage, is very important, but the elements of these torts are evolving and may be difficult to prove against an organization where the main perpetrator of the violation is an individual employee or third party. Vicarious liability for a breach of privacy by a “rogue” employee is possible, but will depend on whether the facts show that the employer organization materially increased the risk of the violation. The current state of the law raises questions about the ability of these claims to effectively provide compensation or deterrence, but in the absence of legislative reform, the progressive development of the law on some of these issues could help to clarify and expand the options available to address ongoing threats to privacy.

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.

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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.418

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 studies0.0000.000
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 head0.016
GPT teacher head0.248
Teacher spread0.232 · 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