From Piracy Panic to Platform Praise (and Back Again): Digitization's Impact on Making, Moving, and Monetizing Music in Canada
Notice bibliographique
Résumé
When Napster, a peer-to-peer file-sharing platform, launched in 1999, the music industry declared a crisis — but this isn't new. For decades, the music industry has feared the presence and popularity of new technologies for the distribution of music commodities, especially ones that circumvent their copyright restricted system, but these technologies have never actually led to the industry's demise. Quite the contrary, in fact. The music industry, despite wide circulation of piracy panic narratives in the early aughts (and before), has never been meaningfully disrupted, and its power structure remains intact today. \n \nWhy, then, in 2017, after the global music industries were reporting an economic upswing from streaming, did major recording industry associations begin issuing reports proclaiming that, in the streaming era, music industries, and by extension musicians, are experiencing a value gap, or low remuneration? To answer this question, this dissertation employs a critical political economy of communication and culture alongside a cultural industries approach to assess the extent to which the availability of digital tools — for distribution, microfunding, and social media — has changed or reshaped the music industry, and in turn musician labour, in Canada. \n \nAlthough discourses about digitization's impact on music have heretofore characterized it as disruptive, that disruption has be interpreted as either productive or restrictive. On the one hand, the industry proclaims that digitization enables piracy, which hurts musicians, so it must be stopped in order for the production and distribution of music to continue; on the other hand, digital optimists argue that musicians no longer need labels, and that discussions about the destruction of the industry are merely indicative of its potential for rebirth as a musician-friendly marketplace, where fan/artist connections are direct and disintermediated. Through evidence drawn from discourse analyses of policy documents, popular press articles, and in-depth interviews with Canadian musicians, this dissertation asserts that neither perspective is correct, and instead demonstrates how digitization has culminated in a simultaneous reduction in recording-based revenue for musicians, an increase in the key responsibilities required for a musical career, and the preservation of a powerful industry hierarchy of economic power.
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Comment cette classification a été obtenuedéplier
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 | 0,001 | 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) | 0,000 | 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 ».