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Record W2598922617

Electronic Trespass in Canada: The Protection of Private Property on the Internet

2006· article· en· W2598922617 on OpenAlexaboutno aff
James E. Macdonald

Bibliographic record

VenueeYLS (Yale Law School) · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and International Contract Law
Canadian institutionsnot available
Fundersnot available
KeywordsTrespassBusinessProperty (philosophy)The InternetInternet privacyLawComputer securityPolitical scienceComputer scienceWorld Wide WebPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

This paper argues that Canadian courts can, and should, adopt electronic trespass as a viable cause of action for the protection of property rights on the Internet. Of course, this conclusion presupposes that property rights in fact exist on the Internet. While American courts have accepted the existence of property rights on the Internet without any real controversy, a significant body of criticism has developed around American jurisprudence. Part III examines the critiques levelled against the assumption of property rights inherent in electronic trespass, and argues that there are property rights that need to be protected on the Internet. Part IV addresses the practical issue of whether electronic trespass is available at common law in Canada. Focusing on the tangible quality of electronic communications and the lack of requirement to show actual damages, this paper concludes that electronic trespass is a viable cause of action in Canada. Despite being a viable cause of action, Part V examines the concerns of the anticommons movement, and considers whether Canadian courts should forgo electronic trespass and adopt an alternative doctrine resembling nuisance. In rejecting such an approach, this paper concludes with a discussion of the importance of consent as a means of imposing rationality on the operation of electronic trespass, and questions the usefulness of legislative reform.

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 score0.993
Threshold uncertainty score0.216

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 studies0.0000.000
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 head0.016
GPT teacher head0.228
Teacher spread0.212 · 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

Citations2
Published2006
Admission routes1
Has abstractyes

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