Platform Practices in the Cultural Industries: Creativity, Labor, and Citizenship
Bibliographic record
Abstract
The rise of contemporary platforms—from GAFAM in the West to the “three kingdoms” of the Chinese Internet—is reconfiguring the production, distribution, and monetization of cultural content in staggering and complex ways. Given the nature and extent of these transformations, how can we systematically examine the platformization of cultural production? In this introduction, we propose that a comprehensive understanding of this process is as much institutional (markets, governance, and infrastructures), as it is rooted in everyday cultural practices. It is in this vein that we present fourteen original articles that reveal how platformization involves key shifts in practices of labor, creativity, and citizenship. Diverse in their methodological approaches and topical foci, these contributions allow us to see how platformization is unfolding across cultural, geographic, and sectoral-industrial contexts. Despite their breadth and scope, these articles can be mapped along four thematic clusters: continuity and change; diversity and creativity; labor in an age of algorithmic systems; and power, autonomy, and citizenship.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".