Extended Collective Licensing as Rights Clearance Mechanism for Online Music Streaming Services in Canada
Bibliographic record
Abstract
According to the statistics compiled by the International Federation of the Phonographic Industry (IFPI), online paid streaming is currently the fastest growing segment of the recorded music market, with a 33% global revenue increase in 2018. Subscription-based services offering legal online paid streaming of music have now reached all corners of the planet. Among the most well-known services are Apple Music, Amazon Prime, Deezer, Google Play, Soundcloud, and Spotify. The creation and continued functioning of such services are contingent on the capacity of the service exploiters to clear all copyrights in the offered music repertoire, for the territory of operation. In practice, rights clearance for online streaming services proves incredibly complex and cumbersome, because every musical work available for streaming on a service is likely to have several right holders: an author, a performer, a record producer, and a music publisher. The number of rights owners entitled to claim rights on a musical work may even be much larger where that musical work was composed by multiple authors or performed by a group of artists, each potentially bound by separate agreements with publishers and record producers. For streaming services wishing to offer a global repertoire, it can be a daunting task to obtain permission for every single musical work, with respect to every territory. In view of the complexity of the music industry, fears of copyright infringement claims are not surprising.\n---\nThe article is further divided into four parts. Section 2 describes the legal framework underlying the online music streaming services in the European Union, the United States, and Canada, where we examine the current licensing practices for online streaming services in the same three jurisdictions. Section 3 describes what are ECLs, first giving an overview of the main characteristics of the ECL model and second, discussing how certain countries have implemented ECLs in practice. Section 4 discusses the challenges posed to Canadian CMOs in meeting the requirements of a legitimate ECL model. The most salient challenge concerns the requirement of representativeness of an ECL granting CMO, but section 4 also examines the safeguards that must be implemented to protect the interests of non-members, the role that the Copyright Board of Canada could be asked to play in the implementation of an ECL regime, and the compliance of ECLs with international obligations in the area of copyright law. Section 5 draws conclusions on the feasibility of using an ECL model for the licensing of online streaming of musical works.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".