Can the drag queen turn back time to give advice to her younger self?
Bibliographic record
Abstract
This article explores the dynamics of inter-generational conversations between queer and trans youth and queer and trans adults through an analysis of a routine segment on the reality show, RuPaul’s Drag Race. At the end of each season, the drag queen contestants are asked to give a piece of advice to photos of their younger selves and, by proxy, the young people who may be watching the show from home. This article positions this segment as a pedagogical moment where the queens are forced into the role of teacher. However, like the categories of child and youth, RuPaul’s request of the queens is not as innocent as it appears. In turning to address children directly, the queens must reckon with cis-heteronormative expectations of how adults can speak to children, about what topics can be discussed, and the tones such conversation must take. To explore these issues, the article examines this segment alongside scholarship about drag and ballroom cultures to place the show within larger contexts of queer and trans kinship structures. Then, turning to queer and trans studies in education and queer and trans childhood and youth studies, the essay examines the factors impacting how and when queer and trans youth and adults are allowed to be in conversation with youth, if at all.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".