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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".