Understanding the creative process in the shaping of an interpretation by expert musicians: Two case studies
Bibliographic record
Abstract
This article presents the methodology and partial results of a study on the creative processes underlying nine performers’ interpretations of a single musical work. Data were collected by videotaping rehearsals and soliciting musicians’ verbalizations of the rehearsal process. Musicians also completed a reflexive questionnaire, and an independent third party observed and described the musicians’ actions. The data were first assessed through content analysis. Subsequently, interview techniques borrowed from phenomenology were used: self-confrontation interviews, which enable the verbalization of the action a posteriori; and explicitation interviews, which facilitate access to the preconscious processes and enable a detailed description of the action. Preliminary results for two performers demonstrate the varied strategies that musicians use to create original interpretations. The strategies observed were congruent with the existing literature and include alternation between divergent and convergent thinking and creative associations. Nevertheless, our results also suggest the existence of a phase step of artistic appropriation specific to each musician.
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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| 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".