Developing a literature-based glossary and taxonomy for the study of mental practice in music performance
Bibliographic record
Abstract
Mental practice refers to the use of imagery as opposed to the physical or motor skills used in physical practice. It is a strategy frequently discussed with regard to the acquisition of skills required for music performance, and recent scientific literature confirms the benefits of mental practice. However, a review of that literature reveals inconsistencies and a lack of clarity in the use of terminology. To better understand this problem of terminology, 33 current studies on mental practice in music performance were assembled and examined for both the quantity and quality of term usage. Terms were identified and recorded using terminology and classification methods from Cabré (1999), and The Pavel, Terminology Tutorial. Terminological records were created for each term appearing more than once in the literature for a total of 83 records. Issues related to frequency of use (repetition), use of multiple terms (synonymy), lack of term definitions, and the need for clarity in term usage (semantic vagueness and ambiguity) were then analyzed using these records. This terminology process resulted in the creation of a glossary of 21 terms and a corresponding hierarchical taxonomy (tree diagram). These tools were developed to clarify the terminology of mental practice in music performance in order to provide a foundation for a more systematic use of the terminology in future research, as well as to assist with comprehension of the existing literature.
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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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.090 | 0.069 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".