Imagining Yourself Dancing to Perfection? Correlates of Perfectionism Among Ballet and Contemporary Dancers
Bibliographic record
Abstract
The present study investigated perfectionism prevalence and its relationship to imagery and performance anxiety. Two hundred and fifty ( N = 250) elite students (66.4% female; M age = 19.19, SD = 2.66) studying mainly classical ballet or contemporary dance in England, Canada, and Australia completed questionnaires assessing perfectionism, imagery, and performance anxiety. Cluster analysis revealed three distinct cohorts: dancers with perfectionistic tendencies (40.59% of the sample), dancers with moderate perfectionistic tendencies (44.35%), and dancers with no perfectionistic tendencies (15.06%). Notably, these labels are data driven and relative; only eight dancers reported high absolute scores. Dancers with perfectionistic tendencies experienced more debilitative imagery, greater cognitive and somatic anxiety, and lower self-confidence than other dancers. Dancers with moderate perfectionistic tendencies reported midlevel scores for all constructs and experienced somatic anxiety as being more debilitative to performance than did those with no perfectionistic tendencies. Clusters were demographically similar, though more males than females reported no perfectionistic tendencies, and vice versa. In summary, the present findings suggest that “true” perfectionism may be rare in elite dance; however, elements of perfectionism appear common and are associated with maladaptive characteristics.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".