ANALYZING PLATFORM POWER IN THE CULTURAL INDUSTRIES
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
Across cultures and contexts, digital platforms like YouTube, TikTok/Douyin, WeChat, and Spotify are fundamentally reshaping both the processes and products of cultural production—from music and news to entertainment and advertising. But, despite considerable attention to the perverse power of algorithms in various spheres of social and economic life, we contend that existing political economic frameworks fail to account for the distinctiveness of the cultural industries. Challenging essentialist theories of platform dominance, this paper argues that claims of platform power need to be qualified in the context of industry- and culture-specific inquiries. Building on research in science and technology studies (STS), software studies, political economy, business studies, and media industries studies, the paper presents a new analytical framework to analyse the evolving power relationship between platforms and cultural producers. It is argued that the decision space of cultural producers in their role as platform complementors is shaped by three key variables: 1) platform evolution, 2) cultural industry segments, and 3) stages of production. The proposed framework makes clear that, while the relationship between platforms and cultural producers is staggeringly uneven and, at times, highly volatile, it should be understood as one of mutual dependence. That is, platforms exert mechanisms of power over the phases of the creation, distribution, monetization, and marketing of culture; but they also furnish space for negotiation and contestation. Acknowledging this requires a framework that is less deterministic and sensitive to the nuance inherent in cultural production.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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