Bibliographic record
Abstract
Abstract The theory of generative art redefines creativity by bridging human intention with the computational power of AI. Historically, creativity has been viewed as uniquely human—a product of imagination and self-expression. Generative art challenges this view, positioning machines not as mere tools but as collaborators. This shift reshapes our understanding of authorship, originality, and technology’s role in artistic expression. At the heart of generative art lies “creative generativity,” which transcends algorithmic reproduction. Although AI can generate unique outputs by recombining patterns from data, creative generativity produces works that defy prediction, extending beyond learned limits. This mirrors human creativity—not through imitation, but by offering fresh perspectives and possibilities. Generative art reveals what humans and machines can achieve together, probing the boundaries of imagination. It also questions traditional power dynamics between humans and technology. Machines, once subservient, now operate with autonomy, introducing spontaneity into the process. While humans set the framework, machines generate the unexpected, creating a collaborative dynamic. This raises ethical concerns around authorship and bias, including the influence of societal data and ownership of AI-generated works. Its interactive nature sets generative art apart, transforming audiences from passive observers to active participants. Many works evolve through viewer input, blurring the lines between creator, artifact, and audience. This participatory model aligns with contemporary ideas of co-creation, dynamically shaping meaning through interaction. By broadening aesthetic possibilities with algorithms and AI, generative art offers new ways to experience beauty, reimagining creativity as a shared journey and redefining what art can be.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".