Bibliographic record
Abstract
Stephen Davies and Jerrold Levinson have each offered accounts of how music can express emotions. Davies’s ‘Appearance Emotionalism’ holds that music can be expressive of emotion due to a resemblance between its dynamic properties and those of human behaviour typical of people feeling that emotion, while Levinson’s ‘Hypothetical Emotionalism’ contends that a piece is expressive when it can be heard as the expression of the emotion of a hypothetical agent or imagined persona. These have been framed as opposing positions but I show that, on one understanding of ‘expressing’ which they seem to share, each entails the other and so there is no real debate between them. However, Levinson’s account can be read according to another—and arguably more philosophically interesting— understanding of ‘expressing’ whereas Davies’s account cannot as easily be so read. I argue that this reading of Hypothetical Emotionalism can account for much of our talk about music in terms of emotions but must answer another question—viz., how composers or performers can express emotions through music—to explain this relation between music and emotion. I suggest that this question can be answered by drawing on R. G. Collingwood’s theory of artistic expression.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".