DESCRIPTIVE STUDY OF COMPETITIVE BATON TWIRLERS
Bibliographic record
Abstract
Alexis Dicks1, Rhiannon Gregory1, Miranda Proctor2, Amanda Trujillo2, Andrew Hatchett1. 1University of South Carolina Aiken, Aiken, SC. 2University of South Carolina, Columbia, SC. BACKGROUND: Competitive baton twirling is a sport that combines elements of gymnastics, dance and ballet while necessitating cardiovascular endurance, muscular endurance and power, hand-eye coordination, spatial awareness, kinesthetic awareness, timing, and choreography. Approximately one million baton twirlers participating in the sport in the United States. Despite considerable participation in competitive baton twirling, little is known about the characteristics of the athletes. METHODS: This work documents demographic and behavioral characteristics of competitive baton twirlers. Questionnaires were completed by 169 female twirlers from across the Unites States and Canada. Questionnaires were sent out via social media and the only excluding factor was that participants had to have participated in competitive baton twirling. RESULTS: Respondents reported a mean ± SD age of 18.07 ± 6.08 y, height of 162.28 ± 6.24 cm, weight of 60.58 ± 32.49 kg, BMI of 22.92 ± 2.34 kg/m2, GPA 3.73 ± 0.3, and years of competing 8.02 ± 1.81 y. All (100%) qualified respondents reported experiencing injury due to competing in or training for baton. The extent of the injuries reported varied greatly. The top five injuries reported consists of bumps and bruises (95.5%), sprained or strained fingers (53.9%), sprained or strained wrist (25.3%), sprained or strained neck (14.6%), sprained or strained back (35.4%). A diversity of training, recovery, hydration, and nutrition habits were also reported. Of the 169 respondents 72% of the twirlers practice four or more days out of the week; 95% employ stretching and mobility for each session and 90% have structured and targeted practices. Over half of the twirlers consider nutrition when training and competing (66%), while 92% consider hydration when training and competing. CONCLUSION: These findings indicate that the competitive baton twirlers that participated in this research are adolescent females, diverse in physical profile, of normal BMI, high academic achievers, dedicated athletes, consistently overcome injuries and train by diverse means.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".