“It’s messy, but it’s real, and it’s so wonderful”: understanding resilience, self-exploration, and advocacy through drag performance
Bibliographic record
Abstract
The modern drag scene is facing significant challenges due to negative media coverage and a contentious political climate, threatening its cultural standing. Comprehensive empirically based studies on drag performers, including participants with diverse gender identities, i.e. drag kings, queens, and ‘bio’ or hyper queens, have remained scarce. This qualitative study investigated the impact of participation in drag on shaping resilience, gender identity, and advocacy among cisgender and transgender performers. Using social media and snowball recruitment, we conducted 11 in-depth interviews with drag performers of varied gender identities, ages, and performance experiences. Reflexive thematic analysis through a constructivist lens revealed three major themes: 1) The Big Drag (Queer) Family (i.e. the creation of chosen families), 2) Me and My Drag (i.e. personal journeys and growth), and 3) The Drag Medium is the Message (i.e. the use of drag as a platform for advocacy and social change). Key findings included significant personal development, strengthened resilience, and enhanced self-expression among performers. These findings underscored the significance of drag, highlighting its role in supporting LGBTQ+ identities in public spaces and its capacity to challenge societal norms, inviting reflection on the power of drag to shape and challenge societal norms.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".