Our Instruments are Our Masks: Developing Communication Skills and Confidence Through Collective Free Improvisation
Bibliographic record
Abstract
This study explores the idea that musical instruments can function as masks in the context of collective free improvisation. Just as masks are used in theatre improvisation to build confidence and facilitate creative expression, musicians can use their instruments in similar ways, increasing their level of comfort and allowing them to connect and communicate with others in ways not available through traditional social exchanges. Through a variety of interviews, questionnaires, and performances, 30 instrumentalists and vocalists participated in this study and shared their experiences performing with their peers. Through analysis of recorded performances, interview and questionnaire responses, it was discovered that the vast majority of participants identified with the idea that their instruments functioned as masks. Most of these individuals believed their instruments helped them express parts of themselves that could not be expressed through other means, and many believed their instruments allowed for the creation of a persona, in which they felt they could “be” someone else when performing. Participants were in agreement that they only felt their instruments functioned this way in the context of collective free improvisation. The strong feelings of connection, confidence, and communication experienced by participants strengthen the case for incorporating free improvisation into music education.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".