Dancing around and through harm: Examining the lived experiences of women of colour with gender-based violence in the Toronto & Kitchener-Waterloo Latin dance communities
Bibliographic record
Abstract
Given the systemic nature of gender-based violence in Canada, as well as the increasing popularity of Latin dance, it is important to better understand the particular and culturally-specific ways gender-based violence manifests itself within the Latin dance community. This research study examines the lived experiences of women of colour with gender-based violence in the Toronto and Kitchener-Waterloo Latin dance communities. Two groups of participants took part in semi-structured interviews: 14 women of colour dancers, and six “Power Players”, leaders in the Latin dance community who are in a position of power (e.g., instructors, organizers, DJs). The data was analyzed using a thematic analysis. The results indicate that gender-based violence in the scene is engrained, normalized and accepted. Women of colour dancers report having to consistently negotiate power dynamics and navigate gender-based violence in the Latin dance community. Study results also show that there appears to be a dissonance between lived experiences of women of colour with gender-based violence and perceptions of Power Players of gender-based violence in the scene. Women of colour dancers experience pervasive racialized sexism; for example, they are asked to dance significantly less than white women. Participants report higher levels of gender-based violence in white-washed dance forms (e.g., sensual bachata). As a result of these intersecting experiences of violence, women of colour dancers have developed diverse and extensive coping skills, to dance around and through harm. These coping skills take significant emotional labour, time, and strategic planning. Recommendations to foster safer spaces in the Latin dance community include practicing community care, upholding organizational responsibility, and integrating embodied consent education.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".