The Benefits of Musical Creation: Improving Education and Encouraging Innovation
Bibliographic record
Abstract
Led by Tim Brady, the Digital Content Initiative (DCI) recently commissioned a research document in order to give academic and scientific support to their arguments as to the value of specialized music. This document includes an analysis and bibliography of a wide range of studies that have been published on the importance and benefits of music and creativity. CNMN members are welcome to freely use this document, and these arguments, when helping to support the cause of creative new music in Canada. Discussion A number of recent studies (see Bibliography below) have demonstrated the value and benefits which can be generated by initiatives supporting musical creation. These positive effects can be perceived in at least three domains: cognitive, social and ethical. For each of these, the contributions of musical creativity extend far beyond the realm of music. 1. The benefits of musical creativity in the cognitive domain: an adjuvant to the development of knowledge and skills A number of studies conducted in the field of cognitive musicology have brought to light the role of learning and practising music in the acquisition of spatial-temporal skills (Hetland 2000). More generally, a number of studies have demonstrated that students who are regularly exposed to creative music, through listening or playing, attained higher-than-average results in all spheres of activity (Johnson and Memmot 2006). Other papers revealed a higher level of aptitude for learning foreign languages or a more effective acquisition of vocabulary in foreign languages (Bygrave 1995) in students who practise music regularly. At the primary, secondary and post-secondary levels, this routine practice also encourages the capacity for concentration and memorization (Schellenberg 2004). In the fields of education and scientific research and in the working world, listening, studying and practising music also promotes inventiveness and creativity (Boulez and Connes 2011). Such findings can be explained by the different types of skills required by listening exercises (particularly through the linking of our faculties of imagination and reason) and by musical practice (coordination, listening to others). In short, exposure to creative music allows individuals to learn how to learn. A number of studies have reported on this function of creative music, which is capable of nurturing in turn the imagination and creativity of those either listening to it or practising it
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".