Aspiring Ballerinas and Implications for Counselling Practice
Bibliographic record
Abstract
Preparing for a professional ballet career requires dedication, discipline, and single-minded focus. But as training becomes increasingly competitive, many dancers must give up their aspiration to become a professional. The aim of this study is to share the stories of elite female dancers who, despite years of intense training, were unable to achieve a professional dancing career. Five women volunteered to tell their stories by participating in multiple semistructured interviews, during which they also shared personal mementos such as dance photographs, pointe shoes, and dance competition medals. Their stories were analyzed thematically and represented with illustrative quotes. Findings suggested that giving up the dream to dance professionally after years of training resulted in many losses; most striking was the loss of identity. Implications for counsellors working with amateur female dancers who were unable to achieve a performing career are discussed.
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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 | 0.021 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.022 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".