Bibliographic record
Abstract
It may take an insider to connect why drag kings still fight for recognition. For the past twenty-five years, I have been dedicated to the art of drag kinging. I have been involved in and created multiple productions across the world. I have researched and documented drag kings since 1999. Through the years, I witnessed how drag kings shaped the concept of the word ‘drag’ itself. Drag kings have always deconstructed gender. To quote Crema from the article, we have always “mind fucked”. Drag kings, even when calling themselves male impersonators, twist the concepts of masculinity, femininity, and gender in highly entertaining and academic ways. So why don’t the majority of drag artists know this? I believe it is because most of us have not seen or read that much about drag kings. The media puts us second after drag queens, especially since the growing popularity of RuPaul’s Drag Race. As drag continuous to evolve, I want to share how kings built the stages and opened the doors for this gender revolution. It is with great pride that I share with you some history and interviews with fellow Canadian kings who helped transform and bring recognition to this art form.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".