From Piracy Panic to Platform Praise (and Back Again): Digitization's Impact on Making, Moving, and Monetizing Music in Canada
Bibliographic record
Abstract
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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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 | 0.001 | 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) | 0.000 | 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".