Bibliographic record
Abstract
Creativity has long been viewed as a uniquely human capacity, rendering fine arts the epitome of human expression. Valued for their aesthetic, intellectual, and emotional significance, fine arts fulfill diverse cultural roles, ranging from entertainment to identity expression and education. The global art market generates over 500 billion USD annually, with museums serving as key public access points (The Business Research Company, 2025). In the face of technological advancement, and specifically the rise of GenAI, creative tasks are increasingly being taken over by machines (De Cremer et al., 2023), calling into question the uniqueness of this characteristically human skill. A significant transformative impact of GenAI is expected in the creative industries—such as music, design, film, dance, publishing, and advertising—where creative professionals (e.g., graphic designers, composers, writers, choreographers) have begun integrating GenAI tools (e.g., text and image generators) into their work practices (Amankwah Amoah et al., 2024). The transformative potential of GenAI in creative work raises hopes of accelerating ideation processes, diversifying artistic outputs by lowering entry barriers for creators, and introducing new aesthetics that may advance art and culture in the long run (Epstein et al., 2023). However, concerns persist about GenAI crowding out the authenticity of human creativity, perpetuating harmful aesthetic and cultural norms, and undermining human-driven inspiration and ideation, all of which fuel ongoing controversy over GenAI’s role in creative work (De Cremer et al., 2023; Epstein et al., 2023). Furthermore, GenAI’s influence in fine arts is expected to extend beyond artistic creation, affecting other stakeholders as new modes of audience engagement and market mechanisms emerge, ultimately triggering a fundamental shift in the societal role of fine arts. This research aims to explore the transformative impact of GenAI on fine arts by examining how it disrupts traditional processes of artistic creation, audience engagement, and the global art market. By analyzing these shifts, we seek to develop a conceptual framework that reimagines the role of fine arts in society, considering new modes of artistic expression, interaction, and valuation. This project will provide insights into the evolving dynamics between artists, audiences, and market structures, offering a foundation for understanding the future trajectory of fine arts in the age of AI. To this end, we propose developing a framework that encompasses the transformed stakeholder interactions and their respective impacts, enabling the creation of digital tools that will reconfigure the digital art world.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".