Shedding light on the stage: The training demands of competitive hip-hop dancers
Bibliographic record
Abstract
Despite the growing popularity of hip-hop in Canada, research exploring the unique stressors and coping experiences of competitive dancers is sparse. As a first step in addressing this gap, the purpose of this study was to take an exploratory approach to better understand the types of demands faced by competitive hip-hop dancers and how they attempted to manage these demands. Eleven participants (5 women, 6 men) who have represented Canada at an international hip-hop competition in the past year each participated in a one-on-one semi-structured interview early in their competitive season. An interpretive description framework informed this study (Thorne, 2016). Findings suggest that competitive hip-hop dancers experience a range of demands surrounding their training context (i.e., athleticism, social comparison, pressure of expectations) as well as their ability to participate in competitive hip-hop dance (i.e., financial demands, balancing work, school, and other commitments). Participants reported using a variety of coping approaches to manage such demands, including: coping efforts centered on demands of the self (i.e., self-care, love of dance), coping targeting training session demands (i.e., team accountability, physical training), coping through the dance community (i.e., sense of family and social support, sharing the load), and efforts to manage auxiliary or compounding demands that influence dance training (i.e., time and financial management strategies, schedule flexibility). Understanding the types of demands faced by this unique population can aid dancers, directors, parents, and practitioners in designing tailored programs and support systems to supplement existing coping efforts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".