Artificial intelligence in aesthetic situation management: new solutions supporting or substituting an art creator
Bibliographic record
Abstract
The article aims to define and describe potential areas of an aesthetic situation in which artificial intelligence may be applied in supporting or substituting roles. Analysing relations between artist, artwork, art recipient, the world of values, and the real world – based on the components of the aesthetic situation theory by Maria Gołaszewska in the Outline of Aesthetics (orig. Zarys estetyki, first published in 1984) and its development by applying the managerial lens by Michał Szostak in the Art of Management – Management of Art (orig. Sztuka zarządzania – zarządzanie sztuką, first published in 2023), allows to define particular universal areas of an aesthetic situation where artificial intelligence may be applied. The central methodological approach is a literature review on an aesthetic situation, aesthetic situation management, and artificial intelligence and its use in aesthetic situation management. The analysis results define two groups of artificial intelligence roles within an aesthetic situation: supporting and substituting. Both roles are described in detail based on aesthetic situation components and their management by an artist who is considered a manager of the aesthetic situation. Limitations of the considerations and directions of future research are defined.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".