Bibliographic record
Abstract
Abstract The Unification of the Arts presents the first integrated cognitive account of the arts that attempts to unite all of the arts into a single framework, covering visual art, theatre, literature, dance, and music, with supporting discussions about creativity and aesthetics that span all of the arts. The book’s comparative approach identifies both what is unique to each artform and what artforms share with one another. An understanding of shared mechanisms sheds light on how the arts are able to combine with one another to form syntheses, such as choreographing dance movements to music, or setting lyrics to music to create a song. While most psychological analyses of the arts focus on perceptual mechanisms alone—most commonly aesthetic responses—the book offers a holistic sensorimotor account of the arts that examines the full gamut of processes from creation to perception for each artform. This allows for a broad discussion of the evolution of the arts, including the origins of rhythm, the co-evolution of music and language, the evolution of drawing, and cultural evolution of the arts. Finally, the book aims to unify a number of topics that have not been adequately related to one another in previous discussions, including theatre and literature, music and language, creativity and aesthetics, dancing and acting, and visual art and music. The Unification of the Arts provides a bold new approach to the integration of the arts, one that covers cognition, evolution, and neuroscience.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".