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Record W2612090563

Dealing with Digital Property in Civil Litigation

2016· article· en· W2612090563 on OpenAlexaffabout
Jeremy de Beer, Tracey M. Doyle

Bibliographic record

VenueSSRN Electronic Journal · 2016
Typearticle
Languageen
FieldComputer Science
TopicLaw, AI, and Intellectual Property
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsProperty (philosophy)CyberspaceLaw and economicsProperty lawTangible propertyNeutralityPolitical scienceProperty rightsContext (archaeology)Intellectual propertyReal propertyLawSociologyComputer scienceThe Internet
DOInot available

Abstract

fetched live from OpenAlex

This article aims to shed light on the conceptual, doctrinal and practical issues regarding digital property law by weaving together several facets of the subject. Legislative schemes for the digital environment have emerged to cover some issues but not others. The debate about further statutory reform to deal with digital property is ongoing. Litigated cases in Canada are becoming increasingly common, and those that arise tend to be complex and significant. With this article, we facilitate better understanding of digital property issues among the bench and the bar, and offer a principled approach to legal policymakers grappling with law reform in this context. The approach we put forward is one based on technological neutrality, in the substantive functional sense not the minimalist notion of media neutrality. We begin by providing important historical perspective on the debate about digital property rights, tracing its evolution over the past 20 years. We then canvass six distinct areas of law where digital property issues are engaged: the legal definition of digital assets as property; the protection of digital property against damage; dealing with digital property in life and on death; digital property and privacy rights; jurisdiction over cyberspace; and the technological regulation of digital property. After discussing the most significant legal developments during the past two decades, we conclude with strategic insights for judges, practitioners and others faced with digital property issues in the future. We show how substantive technological neutrality can serve as a guiding principle connecting each of the digital property doctrines we review.

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: none
Teacher disagreement score0.920
Threshold uncertainty score0.228

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 communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.193
Teacher spread0.184 · 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 routes2
Has abstractyes

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