Being the Other in Electronic Narratives: Representation and normalization of queerness in video games
Bibliographic record
Abstract
Joining various critical discourses, notably critical play (Flanagan, 2009), feminist and queer video game theories (Ruberg and Shaw, 2017), our project is interested in the potential of digital literary works (e.g., visual novels, video games) in raising awareness towards social issues, such as equity, inclusivity, and diversity. Analyzing digital literary works as text-objects, we can question them and the discourses they transmit through their procedural rhetoric (Bogost, 2008). With the growing interest in empathy-driven games in the industry and their mobilization of social issues, especially those related to queerness, we decided to explore these designed experiences and the ways in which they may provoke (or not) awareness in players. In this article, we analyzed through a socio-semiotic approach two video games, drawn from a larger queer video game corpus, dealing differently with social issues such as representation and normalization of queerness, to further understand their potential as literary works.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".