Freeman, Damien. Art's Emotions: Ethics, Expression and Aesthetic Experience. McGill-Queen's University Press, 2012, xii + 210 pp., $27.95 paper.
Bibliographic record
Abstract
An unending series of philosophers since Plato has been concerned to analyze and explain relations between art, the emotions, and moral awareness. Recently, however, the volume of first rate work on this theme has dramatically increased. Just to mention a few, Mette Hjort and Sue Laver's landmark collection of papers, Emotions and the Arts, appeared in 1997; Derek Matravers's Art and Emotion in 1998; Martha Nussbaum's Upheavals of Thought: The Intelligence of Emotions in 2001; Jenefer Robinson's Deeper Than Reason: Emotion and Its Role in Literature, Music and Art in 2005; Berys Gaut's Emotion and Ethics in 2007; and, in 2012, this new, ambitious, and richly rewarding book by Damien Freeman. There is a profoundly serious rationale behind all these studies, one that relies on the cross fertilization of subdisciplines, but only as a means to establishing important, underappreciated truths about art and our experience of it. They focus on ways we invest emotionally in artworks, derive emotional benefits from them, and incorporate these emotional ingredients into aesthetic experiences that can have a significant bearing on the leading of a rewarding life. Freeman is not alone in claiming that cultivating the kind of aesthetic experience that certain artworks afford us conduces to powerful moral consequences.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.016 |
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".