Bibliographic record
Abstract
Following recent work by transgender studies scholars that has questioned the relationship between queer theory and trans studies, this essay considers how glitch video games, which have previously been considered to be part of the “queer games movement,” use the glitch as a way of representing transgender life. I survey three glitch games, Problem Attic (2013, Liz Ryerson), Strawberry Cubes (2015, Loren Schmidt), and Anatomy (2016, Kitty Horrorshow)—each of which uses the glitch as an expressive visual aesthetic, remediating the analog artifacts of signal noise or error as a sonic and visual quality, as well as a game design principle. These games place an emphasis on the body as that which glitches, exploring the bad feelings of trans embodiment, including dissociation and dysphoria, as well as demonstrating how the glitched body can be both desired and transformative. In the final section, this essay considers how transgender artists and the ways their work foregrounds glitch as an operation of the body are integral not only to glitch art history, but also to video game development more widely, exploring the influence of glitch aesthetics and game design in Pony Island (2016), a glitch video game by cisgender designer, Daniel Mullins.
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 | 0.000 | 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) | 0.001 | 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".