Bibliographic record
Abstract
Driven by exacting methods and hard data, this volume reveals gender dynamics within the dance world in the twenty-first century. It provides concrete evidence about how gender impacts the daily lives of dancers, choreographers, directors, educators, and students through surveys, interviews, analyses of data from institutional sources, and action research studies. Dancers, dance artists, and dance scholars from the United States, Australia, and Canada discuss equity in three areas: concert dance, the studio, and higher education. The chapters provide evidence of bias, stereotyping, and other behaviors that are often invisible to those involved, as well as to audiences. The contributors answer incisive questions about the role of gender in various aspects of the field, including physical expression and body image, classroom experiences and pedagogy, and performance and funding opportunities. The findings reveal how inequitable practices combined with societal pressures can create environments that hinder health, happiness, and success. At the same time, they highlight the individuals working to eliminate discrimination and open up new possibilities for expression and achievement in studios, choreography, performance venues, and institutions of higher education. The dance community can strive to eliminate discrimination, but first it must understand the status quo for gender in the dance world.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".