Bibliographic record
Abstract
Abstract The term “aesthetic emotion” was treated differently by scholars from the late 19th and early 20th centuries compared with those from the 21st, and there is much to learn from the “early” group. William James distinguished “subtler emotions” that encompass aesthetic, scientific, and ethical qualities, in contrast to “coarse emotions,” such as happiness or sadness, which are part of everyday life. Others, such as Bosanquet, Clay, Bullough, and Dewey described underlying processes that shape aesthetic experiences during episodes of creation and reception. Their insight is that everyday emotions are elevated to abstract and universal levels during aesthetic episodes, much like Aristotle described more than 2,000 years ago. Later researchers, such as Menninghaus and colleagues, were influenced by the “cognitive turn” and treat “aesthetic emotions” as hypothetical constructs whose independent existence is predicated on word frequencies associated with stimulus ratings. The precision of current empiricism can benefit from incorporating rich theoretical musings of the past about aesthetic processes. A comprehensive model should integrate processes related to aesthetics and emotion during creation and reception episodes. Formal properties of art or literary works can stimulate feelings of pleasure or excitement. The subject matter can offer suggestions that elicit personal connections and related emotions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".