What is the influence of music on performance in practice and competition among university competitive fencers?
Bibliographic record
Abstract
Fencing as a sport and music as an expressive form are two topics that may seem very distant in comparison, but both have many aspects that are intertwined. The purpose of this study was to understand how music is used within practice and competition settings and how rhythm, tempo and timing in fencing might be influenced by music. This study used grounded theory and its three-phase thematic analysis and applied a social-constructivist lens. The research question was: What is the influence of music on performance in practice and competition among university competitive fencers? The participants were interviewed using semi-structured interviews and the researcher kept retrospective notes on observations as an insider to the fencing community. The main findings were split into two groups that included practice and competition. Practice music influence showed that music was used to increase motivation but could also cause distraction from the practice. It also showed how one learned to develop fencing rhythm using music, and how auditory cues from music and from saying sounds that correspond to physical movements help with development of timing. Other findings were that fencers have practice structured around the way they learn, moving from learning in parts to wholes or easy to complex. Also noted was that each weapon has its own style that is free to be discovered and developed. Competition music influence was discovered to be almost non-existent other than for the use of pre-competition preparation and was used sometimes between bouts for relaxation purposes. Other findings were that due to external stressors, fencers tend to not be aware of what their body is doing. In their minds, the action feels correct, but it might have been too big or small or too fast or slow. Also, partner rhythm within a competition is difficult to manipulate as both opponents are trying not to follow each other’s footwork. Music seems to have an influence on those who use it to their advantage, but is connected to the athletes, coaches, and their way of learning.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".