VIRTUAL MADNESS, CULTURAL REALITIES: A CRITICAL CROSSED EXAMINATION OF PSYCHOSIS REPRESENTATION IN LAYERS OF FEAR AND HELLBLADE SENUA'S SACRIFICE AND THEIR RECEPTION IN ONLINE COMMUNITIES
Bibliographic record
Abstract
This article presents a thorough and comparative analysis of two video games, Layers of Fear (LOF) and Hellblade: Senua's Sacrifice (HSS), exploring their representations of madness. By combining an examination of relevant scientific literature with an analysis of Steam and Reddit forums, the study provides a comprehensive overview of gamer reactions and the ethical implications of developers' representation choices. While LOF takes a horror-oriented approach, eliciting mixed reactions, HSS stands out for its nuanced conceptualization resulting from collaboration with experts and individuals living with psychoses. The article underscores the importance of sensitivity, respect, and empathy toward psychosis in gaming representation, highlighting the diversity of approaches in addressing mental health through video games. Additionally, the discussion delves into fundamental questions regarding artistic versus realistic representation of madness, challenging norms and emphasizing the poetic power of video games as a medium for artistic expression and understanding of the psychotic experience.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".