Bibliographic record
Abstract
This paper presents a new account of the pleasure we take in art. It distinguishes first between two types of pleasure. Relief or r-pleasure is transient and marks the end of a state that is difficult to maintain. Facilitation or f-pleasure accompanies an activity and lasts as long as the activity does. It motivates this activity and optimizes it by activating a suite of preparations and modes of acting specific to the activity. Aesthetic pleasure is a distinct form of f-pleasure. It arises from mental engagement with an object, and motivates and optimizes such engagement by activating a nexus specific to it. Aesthetic pleasure acts in two ways. In the short run, when we are in contact with a particular artefact, it keeps aesthetic engagement running smoothly. Over longer periods, it plays a critical role in shaping how we engage with objects to get this kind of pleasure from them. This account is yoked to a broadly functional understanding of art: it is not the nature of the object that makes it art, but the nature of the response that it is designed to elicit. The view does not, however, rest on individual psychology alone, as some other functional accounts do. Crucially, it is argued that shared cultural context is a key determinant of the pleasure we derive from aesthetic artefacts. The pleasure of art is always communal and communicative.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".