Can Expert Dancers Be a Springboard Model to Examine Neurorehabilitation via Dance?
Bibliographic record
Abstract
Abstract Neuroimaging research in dance investigates neural signatures and networks to better understand the effects of dance on the brain. From studies of experts showing regions involved in learning choreography to looking at how long-term practice of dance may be reflected in structural and functional brain changes, a growing body of evidence suggests that engagement in dance may be both neuroprotective and neurorehabilitative across the life span. This body of research provides a strong basis and explanatory framework for the emerging trend of using dance in the treatment of neurodegenerative disorders, such as Parkinson’s disease. We investigate elements of dance that contribute to these findings, and review recent research in this nascent field, starting with a study involving expert breaker Ken Swift. We also look at applications such as Popping for Parkinson’s®, an international project that specifically draws on the cultural and movement vocabulary of Hip Hop. Linking research on expert dancers and the experiences of older adults participating in dance, this chapter presents a comprehensive model of the ways in which dance can contribute to health and well-being across the lifespan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".