Self-Compassion: A Potential Buffer in an Evaluative Dance Environment
Bibliographic record
Abstract
Common characteristics of the dance environment, including mirrored settings, tight-fitted clothing, and evaluation by others create an atmosphere that can negatively influence a ballet dancer’s body image and act as a barrier to participation (Radell, Adame, Cole, & Blumenkehl, 2011; Tiggemann & Slater, 2001). Thus, it is important to identify resources that can buffer against negative self-perceptions in an evaluative dance environment. The purpose of this study was to explore self-compassion in relation to self-evaluative thoughts and behaviours in an evaluative ballet environment. Participants (N = 57 women undergraduate students; Mage = 20.59 years, SD = 3.81) completed an online questionnaire containing measures of self-compassion, social physique anxiety (trait and state versions), fear of negative evaluation (trait and state versions), as well as reactions, thoughts, and emotions to a hypothetical first day of beginner ballet class scenario consistent with the common characteristics of the dance environment. Self-compassion was negatively related to trait and state social physique anxiety, trait and state fear of negative evaluation, total negative affect, personalizing thoughts, and catastrophizing thoughts, as well as positively associated with behavioural equanimity and thoughts of equanimity. Finding self-compassion to be associated with lower neegative self-perceptions within the context of an evaluative beginner ballet class replicates past correlational research and advances the literature by contextualizing self-compassion to a specific evaluative environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".