Transmissions of Music on the Internet: An Analysis of the Copyright Laws of Canada, France, Germany, Japan, the United Kingdom, and the United States
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".