Echoes of Madness: Exploring Disability and Mental Illness in Hellblade: Senua’s Sacrifice
Bibliographic record
Abstract
Video games are known for many things, but nuanced portrayals of characters with mental illness might not be one of them. This trend, however, has gradually started to shift with games like Hellblade: Senua’s Sacrifice, which aim to convey a genuine experience of mental illness to the player. Through a close reading of different instances in the game, this paper shows how Hellblade complicates the usual sanist ideas seen in most other games by taking an ambiguous stance, using mental illness as a representational tool. Furthermore, it avoids some of the more sensationalist and problematic tropes often employed in such representations, like the supercrip and the Cartesian divide of the body and mind. In order to show this, we have employed Mitchel and Snyder’s concept of narrative prosthesis to demonstrate how the game does not in fact rely on Senua’s disability as an exotic feature of the narrative to hook players in. By combining insights from disability and mad studies, we show how this game is a step in the right direction when it comes to challenging the perceptions of mental illness prevalent in pop culture.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".