Artificial Intelligence and the Social Scientist: The Mediation of AIfied Creative Sites
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 mediating role of social scientists in the cultural integration and regulation of artificial intelligence (AI), with a particular focus on the creative industries. Drawing on an ethnographic case study within a film cooperative, it identifies four modalities through which social scientists become enrolled in AI-related organizational processes: as middlemen linking theory and practice, as distributors facilitating the flow of agency, as coordinators bridging innovation and appropriation, and as hosts observing the reproduction of technical skills. Situated at the intersection of Science and Technology Studies (STS) and Media Studies, the article rethinks mediation not as passive translation, but as an active montage of fragmented meanings, practices, and actors. It argues that AI is not merely an object of study but a distributed assemblage whose significance emerges through situated associations. By articulating how social scientists engage with AI through organizational consultation, cultural programming, and collaborative experimentation, this paper reframes the sociology of AI as a field of strategic, reflexive, and creative intervention. In doing so, it highlights the importance of problematizing mediation as a relational practice that connects cultural actors, technologies, and institutions in the evolving ordering of artificial intelligence.
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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