Bibliographic record
Abstract
I came to drag through theatre practice. I am a trained playwright and together with my husband, Cameron Mackenzie, I run Zee Zee Theatre, a company with a mandate to share intimate and human stories, with a focus on amplifying voices from the margins. Over the years I have been working continuously to bring my drag and theatre practices closer and closer together with drag-theatre hybrids appearing in our company's season as well as club events featuring drag artists. In this article I reflect on my personal history as a theatre artist, producer, and drag performer. From this vantage point, I consider the multitude of ways that drag and theatre intersect and have enriched my own artistic practices. Bringing together diverse audiences and artists from the worlds of theatre, drag, and queer club nights, I have been privileged to witness the incredible power of collaboration between these groups. More recently, as a queer parent, I’ve seen how theatre and drag can merge to carve out new and exciting ways to bring queer performance to the ever expanding audiences of queer families. This article traces these experiences and stands as a testament to the generative power of collaboration between drag and theatre.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".