Playing in Drag: A Study on Gender In Virtual and Non-Virtual Gaming
Bibliographic record
Abstract
This project explores hybrid avatar identities and gender through an analysis of how players navigate gender in games that are popularly considered to be “for girls only” or “for men only”. It also considers the choice of avatar gender that players make in game, and their reasons for making that choice. Finally, it looks at the reported experiences of playing characters of both genders in both online visually rich immersive game environments, as well as leaner table-top RPG play. Using Butler’s gender trouble, we analyze how gender in game play can be both like and unlike drag performance. We also use the frame of gender trouble to consider the question of whether players who openly play games contrary to social expectations, or play an avatar of a different gender, are engaging in a transgressive act. Data was collected through a discourse analysis of online forums, participant observation, and autoethnographic reflection.We find that when the act of play itself is transgressive, there are opportunities to reach a community with a message that challenges dominant ideas of gender. However, the reasons why people choose to play a specific game or avatar within that game are very complex, and the content of the game, along with the reasons people choose a gendered avatar, or how they relate to the avatar both support and subvert dominant gender 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".