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Record W3123896986

Transmissions of Music on the Internet: An Analysis of the Copyright Laws of Canada, France, Germany, Japan, the United Kingdom, and the United States

2001· article· en· W3123896986 on OpenAlexaffabout
Daniel J. Gervais

Bibliographic record

VenueSSRN Electronic Journal · 2001
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIntellectual propertyLawThe InternetPolitical scienceAppealCopyright ActIntermediaryOfficerSection (typography)TrademarkCopyright lawBusinessAdvertisingComputer science
DOInot available

Abstract

fetched live from OpenAlex

This Article examines the status of copyright laws in several countries as they pertain to transmissions of music on the Internet. Because the exact legal ramifications of music transmissions over the Internet are currently unclear, the Author compares copyright laws of six major markets and examines the potential application of the copyright laws and other rights that may apply. The Article also discusses rules concerning which transborder transmissions are likely to be covered by a country's national laws, as well as specific rules applying to the liability of intermediaries. Next, the Article summarizes the comparative findings and discusses the relevant nuances that exist among the countries covered. Finally, the Article applies its findings to several real-life examples and details the practical impact of current and future copyright laws on the varying fact patterns. * Associate Professor, Faculty of Law (Common Law Section), University of Ottawa. dgervais@uottawa.ca. Former Head of Section at the World Intellectual Property Organization (WIPO); Legal Officer at the World Trade Organization. The Author wishes to thank Ms. Marie-Pierre Simard and Goldie Bassi for their assistance in the research necessary to prepare this paper. 34 Vanderbilt Journal of Transnational Law; November, 2001 1364 Table of

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 score0.839
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.211
Teacher spread0.194 · 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
Published2001
Admission routes2
Has abstractyes

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