Windows to wellbeing: Insights from music performance science
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Keynote presentation: The emotional life of performers is complex. To perform with freedom, spontaneity, and creativity, they must be prepared to take risks and ‘feel the fear’, but they must also find ways to manage their fear so they can be physically and mentally capable of expressing themselves freely and creatively. A nuanced approach is needed to help performers navigate this territory. Applying interventions to enhance performance requires us to be cognisant to the performer’s stage of development and performance ambitions. These are situated within a myriad of biopsychosocial factors and educational and occupational demands that collectively influence musicians’ health across their lifespan. In this talk I draw from clinical, research and teaching practice to discuss windows to psychological wellbeing - tried and tested approaches to performance anxiety management. My explanation explores basic psychological needs, self-regulated learning principles, performance routines for emotional regulation, and psychological flexibility. Strategies will be suggested for musicians to implement in their performance practice.\n\nReference: \nOsborne, M.S. (2021, 27-30 October). Windows to wellbeing: Insights from music performance science. Keynote presented at the International Symposium of Performance Science on “Performance Health and Wellbeing”, Montréal, Canada.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it