Extended Collective Licensing as Rights Clearance Mechanism for Online Music Streaming Services in Canada
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».