A scoping review of empirical research on dance imagery
Bibliographic record
Abstract
Given the evolution of dance imagery research over the past several decades, an up-to-date synthesis of knowledge is critical to advancing research and evidence-based practice. Therefore, a scoping review was conducted to examine: (1) what is known about imagery as a performance enhancing technique for dance specialists, and (2) what research methodologies are used in this field? A total of 53 studies were included in the review. Collectively, 3548 participants were involved, including dancers, dance teachers, a choreographer, and non-dancers. Emerging adults (19–30 years) who danced professionally were primarily examined. Study objectives were categorized as exploring: (1) the impact of imagery on physical development, (2) imagery use across disciplines or between dancers, (3) the nature of dancers’ imagery use and development, (4) the use of imagery to elicit physiological responses, and (5) dance imagery questionnaire development and validation. Operational imagery definitions varied in context, with most specific to the motor domain. For methodology, studies were predominantly quantitative and cross-sectional. Taken together, several practical considerations can be gleaned from the current findings including inter-disciplinary collaborations, appropriate use of theoretical frameworks, examining more diverse samples, and greater use of dance-specific questionnaires.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".