Tracking the creative process in music: New issues, new methods
Bibliographic record
Abstract
Introduction to Musicae Scientiae special issue entitled 'Tracking the Creative Process in Music'.An increasingly complex image of the creative process in music has emerged in recent years. Composition remains, of course, a major part of the study of the creative process, but equally performance, improvisation, sound engineering, as well as many other areas and roles, are key to its understanding. Creativity is not just the stuff of western art music: virtually any genre or culture can be rich terrain for its study. We also need to investigate the psychology of musical creativities instead of thinking of creativity as a monolithic category. Moreover, the boundaries between disciplines, objects, and methodologieshave been blurred.The present issue is intended to reflect, at least partially, on how the aforementioned changes, especially current developments in the study of performance as and in the creative process, affect the very definition of our objects and methods. It draws on empirical research presentedat the 2nd Tracking the Creative Process in Music conference that was held jointly at the Faculty of Music, Université de Montréal,and the Schulich School of Music, McGill University, in September 2013.
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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".