“Still colourblind”: using mapping and interviews with former students to explore racial and ethnic diversity in university dance programs in Canada
Bibliographic record
Abstract
Dance is a form of cultural expression spanning all nations, showcasing ceremony, community, and/or performance for pleasure (Anderson, 2018). However, within the university setting, there is a hierarchy of genres, with superiority being based on race and ethnicity. As dance can be a connection to culture and ancestry, being given permission to explore cultural identity through movement may yield a deeper understanding of self, culture, and their significance on society (Zhang et al., 2020). This interpretive study uses critical race theory (CRT) as a lens for deciphering the system of oppression felt by university dance students with regard to representation, recruitment tools, and curriculum. Reflexive thematic analysis (Braun & Clarke, 2013) is used for web-based content and semi-structured interviews. This study seeks to answer: How is racial and ethnic diversity perceived by students in dance departments in Canadian universities? Web-based data coupled with interview answers confirm a lack of racial and ethnic representation in faculty members, student base, and course content which directly affects career viability, research opportunities, and stunted educational evolvement. The results revealed themes including: a) early indoctrination of acceptability, b) how career goals changed with exposure, c) witnessing of hegemony or diverse representation, c) the importance of mentorship, and d) lack of course diversity. Participant experiences have left them with little hope of change in this context, without great effort at all levels of Canadian dance.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.035 | 0.017 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".