Got to be Real: Queering Reality, Identity, and Audience Affect on RuPaul’s Drag Race
Bibliographic record
Abstract
The meteoric rise of RuPaul's Drag Race in popular culture has given the extravagant subculture of drag a mainstream platform through reality television.Drag Race constructs queer identities and drag performances to make them palatable for both LGBTQ+ and cisheterosexual audiences alike.To understand how audience members consume and understand queerness and drag, I interviewed self-identified fans of Drag Race and recollected my own connections with the show.Coupled with queer and affect theory, these interviews inform my analysis of Drag Race and how it depicts queerness and the impact it has on viewers.Audiences are endeared to RuPaul's drag reality through the queering of the reality television genre, the onstage/offstage/backstage depictions of gender and sexuality, and affective relationships they build with the show.RuPaul's Drag Race invites fans behind the curtain of drag performances to be entertained by personal narratives of queerness beyond the spectacular drag persona.McHarge 1 Racers, Start Your Engines: Introduction "Bitch, we all have a place here and so you might as well have enjoyed the show."Said Bob the Drag Queen to me after the "Siblings Rivalry Tour" show on September 14, 2019 at the Algonquin Commons Theatre in Ottawa, Ontario.I was one of the many-and I mean many-young, white, and femme-presenting attendees waiting in line after the performance to get a chance to briefly meet the stars, Bob the Drag Queen and MonetXChange, as they signed merchandise bought before the show.After emerging from the shadows of the theatre and into the fluorescent light of the lobby, I stood my place in line to have my obnoxiously large "Sibling Rivalry" fan signed by the fabulous drag queens who had just put on an incredible show.Bob and Monet spent the better part of two hours lipsyncing, dancing, telling jokes, and excessively sweating in front of a wired and receptive audience.Starstruck by seeing two strong contestants from one of my favourite programs, RuPaul's Drag Race, I spent the entire line-waiting time racking my brain for the perfect quip to tell the queens as they sign my fan.Should it be funny or sincere?Brief or winded?Bold or conventional?I decided I didn't have nearly enough time to explain to the queens how much drag as an art form means to me, or to even tell them that their work inspires my research and passion in academia.My turn came.I ambled forward, unfurled my fan and handed it over.Monet complimented my signature velvet shirt and drag show makeup.I gushed over how enjoyable the show was, and settled on saying something pithy and to the point, "I'm just another anonymous fan, but I had a great time."Bob, towering over me and still sweating from the final number, looked me in the eyes and told me that even though I was one single person in the audience of their show on a North American tour, I still mattered.Prior to attending the show, I had just moved to Ottawa to begin my Master's degree at Carleton University.I moved to a new city to live with two unknown roommates, and study at a
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".