Artificial Intelligence and the Social Scientist: The Mediation of AIfied Creative Sites
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.
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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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.049 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".