Ballroom Refuses to Burn: Exploitation versus Community Education in Documentaries about Voguing and the Ballroom Community
Bibliographic record
Abstract
Voguing and ballroom culture—unique forms of performance art which were incredibly important to the survival and artistic expression of LGBTQ+ Black and Latine communities during the height of the HIV/AIDS epidemic—have been captured and preserved in numerous documentaries. Building on a survey of the history of LGBTQ+ ballroom events and pageants in the United States, I then define the ballroom community as it is known today, discussing the inception of the community in the mid-twentieth century as a response to the racism faced by Black and Latine performers in the queer pagent competitions of the late nineteenth and early twentieth centuries, and the impact of the HIV/AIDS epidemic on the community. I then explore the practice of documenting this community, comparing explotive media such as the documentary Paris is Burning (1990), the television show Pose (2018), and music by Malcolm McLaren and Madonna to community-based, ethical media such as Tongues Untied (1989), Black Is…Black Ain’t (1995), and How Do I Look (2006), highlighting the key differences between these portrayals. Ultimately, I argue that community-based media is invaluable, countering the racism and queerphobia found in the more mainstream, exploitive examples with authentic portrayals of the ballroom communsity’s resilience and connectedness.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".