Rising tides? Data capture, platform accumulation, and new monopolies in the digital music economy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article examines the roles of platform-based distribution and user data in the digital music economy. Drawing on trade press, newspaper coverage, and a consumer privacy complaint, we offer a critical analysis of tech-music partnerships forged between Samsung and Jay-Z (2013), Apple iTunes Store and U2 (2014), Tidal and Kanye West (2016), and Apple Music and Drake (2017). In these cases, information technology (IT) companies supported album releases, and music was used to generate user data and attention: logics of data and attention capture were interwoven. The IT and music industries have adapted their business strategies to what we conceptualize as platform-based capital accumulation or ‘platform accumulation’, and models centred on controlling access and extracting rent have enabled the emergence of new monopolies and IT gatekeepers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| 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 it