“THE CREATURE ITSELF IS NASTY, BUT NOTHING REALLY COMPARES TO THE BUILDING OF DREAD BEFORE YOU EVER GET TO IT”: ONLINE PLAYER AND DEVELOPER COMMENTARY ON FEMALE MONSTROSITY IN VIDEO GAMES
Bibliographic record
Abstract
Gender representation in video games has long been a fraught topic of discussion within online gaming communities. In game scholarship, analysis of the usually harmful tropes and trends of female representation has resulted in countless studies demonstrating that video games are often a regressive medium in terms of representation, privileging heterosexual white male subjectivities and erasing, marginalizing, or even vilifying anyone outside of that specific demographic. These conversations and scholarly studies tend to focus on the representation of human women, especially as victimized damsels-in-distress. Considerably less work has been done to analyse the portrayal of villainous and monstrous nonhuman women in games, even though countless science fiction, fantasy, and horror games feature these kinds of characters. Many of these games utilize harmful tropes and design practices related to female villainy and monstrosity, thereby reinforcing misogynistic ideologies. With the understanding that gender representations in games can have deep cultural ramifications, especially as they intersect with representations of race, sexuality, queerness, body size, disability, mental illness, and age, this paper examines online player and developer discourse regarding female-coded monsters from a selection of commercially successful “AAA” video games. The intent of this project is to contribute to ongoing scholarship on monstrosity in games by looking at how developers explain and justify their design processes in online interviews and forum posts and how players/fans articulate their attitudes towards and reception of these monstrous creatures.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".