The Doors of Perception: Horror Video Games and the Ideological Implications of Ludic Virtual Reality
Bibliographic record
Abstract
With the ‘perfect marriage’ between horror and video games has come a concomitant cultural studies discourse surrounding these games. Previously, while Carol Clover insisted that identification through the perspectival screen is poorly understood, she seminally argued that slasher horror in particular allows for more progressive gender identifications. More broadly, and somewhat conversely, Carly Kocurek observes the most reactionary effects of the horror genre’s reduction of cultural ‘others’ to monsters and the problem with their prurient dispatch in video games. Lastly, Tammy Lin argues that the experience of virtual reality (VR) significantly heightens the experience of horror. In concert, these imply that VR should heighten the ideological effects and gender identifications identified by both Kocurek and Clover, for better or for worse. This paper examines the ways in which both ostensibly reactionary and progressive ideological elements have migrated into horror video games and the implications of VR on this phenomenon.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".