Yolanda Valentino: reiterating and criticizing Latina stereotypes through drag performance
Bibliographic record
Abstract
This article explores the drag performance of Yolanda Valentino, a Brazilian woman, who self-identifies herself as white before migration and as racialized post-migration. Yolanda also self-identifies as a drag queen, in Canada, and, more precisely, The Hot Room, a queer and queer-friendly bar and nightclub. I emphasize Yolanda’s drag performance as it points to the larger issues that Latinas, as women from Latin America who share racialized commonalities, may experience in the city. Moreover, my interest consists in highlighting Latina stereotypes, as they represent an element of interest for studies on Latin America. I focus on her show based on Latina stereotypes - like the idea of Latinas as women who are only good for domestic work -, to analyze how such stereotypes are taken up in the nightlife of multicultural Ottawa, even in contradictory ways. I claim that the policy of multiculturalism has helped perpetuate Latina stereotypes in Ottawa with implications for queer/non-queer Latinas. Moreover, I use Gloria Anzaldúa’s (2012) border theory to analyze Yolanda’s drag performance as a performance able to challenge specific dominant social norms related to gender, race, class, and sexuality, as well as capable of contesting specific policies like the policy of multiculturalism and Ottawa’s status quo.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".