IN(TER)DEPENDENCE IN PLATFORMED CREATION
Bibliographic record
Abstract
In recent years, a variety of cultural industries have been transformed by platformization – a process in which technology companies serve as intermediaries connecting different parties (most importantly cultural producers and audiences) through websites and applications. From music to book publishing, movie production, and the visual arts, cultural production has undergone massive changes due to platforms such as Spotify, Apple Music, Amazon, Goodreads, YouTube, Facebook, Instagram, TikTok, Etsy, etc. In most cultural sectors, creators now have to grapple with the platforms that make their work visible to online audiences. This often means paying close attention to the quantitative infrastructure of platforms, namely their algorithms and analytics, which drive visibility and commercial success. This panel examines what these economic and technological changes imply for the independence of cultural production. Classical studies of culture often emphasized the role that the values of independence and autonomy play in shaping artists’ worldviews and practices. From Bourdieu’s analysis of “fields of cultural production” as “the economic world reversed” to Becker’s theory of “art worlds” where internal dynamics redefine external constraints, or the Frankfurt School’s critical take on the demise of aura through mechanical reproduction, sociological approaches have paid close attention to the threats to independence emerging under modern capitalism. In fact, most classical sociology saw cultural producers in the mass cultural industries as having little independence, often assuming (sometimes without empirical research) that the massification of culture would destroy original and critical art works. Here we revisit the question of independence in the context of platformed creation – a term that embraces all forms of cultural production that are mediated, in part or completely, through digital platforms. By bringing together scholars studying different aspects of platformed creation and reflecting on the concept of independence through diverse disciplinary lenses, we ask: what does independence look like in the context of platformed creation? What are some of the theoretical and methodological tools available to scholars for making sense of cultural, economic, and technological independence in the case of platformed creation? And how do these evolving forms of independence affect the kinds of art works and cultural tropes that circulate online? These different studies aim to put the concept of independence in dialogue with the question of interdependence (among cultural producers, audiences, and platforms) in a mediated digital world.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".