ANALYZING PLATFORM POWER IN THE CULTURAL INDUSTRIES
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".