Bibliographic record
Abstract
My intention in this article is to clarify the concept of aesthetic experience. It offers a definition and presentation of the stages required to actualize this phenomenon. To begin with, we will examine the various conceptions of aesthetic experience, through the best known authors in the area of aesthetics. Through the analysis of these conceptions, we will build the foundation of an aesthetic experience model. This will enable us to better understand what occurs within individuals when they have an aesthetic experience. Thus, it will illustrate the stages required to carry out this process and the behaviors in which it is manifest. Ultimately, the aim is to develop a tool with which to evaluate clearly the degree of realization of the aesthetic experience in subjects, within the educational framework of aesthetic sensitivity. Therefore, we will review the literature by grouping the various conceptions of aesthetic experience of authors who have influenced the area of aesthetics the most in the last ten years, with recourse to secondary and even tertiary sources. This is a synthesis of a subject that is extremely vague and difficult to organize. In fact, there presently exists a multitude of aesthetic theories which emerged in Europe since the 18th century, a time in which we began to consider aesthetics as a separate discipline. The authors studied here are for the most part American authors because of their proximity to us and to our North American lifestyle.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".