Artificial intelligence in aesthetic situation management: new solutions supporting or substituting an art creator
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
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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