A Brief Discussion on Improving Stage Performance Quality for Music Major Students: Dealing with Performance Anxiety
Bibliographic record
Abstract
This paper, through an analysis of relevant literature, explores the causes and effects of performance anxiety and proposes comprehensive coping strategies for performance anxiety in university music students. Performance anxiety in music primarily stems from the performer's perfectionist tendencies, negative self-suggestions, and insufficient personal performance skills. This anxiety not only affects the performer's psychological state but may also lead to physiological stress reactions, thereby impacting their technical execution and stage performance. Therefore, music major students need to avoid excessive perfectionism, recognize the imperfections of performance, and develop a belief in imperfect perfectionism. Secondly, building confidence is key to overcoming anxiety; students need to actively build confidence and acknowledge their efforts and time invested. Additionally, thorough preparation is essential; anxiety can be alleviated through repeated practice and simulated performances to gradually adapt to the performance environment. Through these analyses, it is hoped that the impact of anxiety on music major students' learning can be improved, promoting the healthy development of music performance art.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".