Carceral violence at the intersection of madness and crime in <i>Batman: Arkham Asylum and Batman: Arkham City</i>
Bibliographic record
Abstract
The action-adventure video games Batman: Arkham Asylum (2009) and Batman: Arkham City (2011) draw on familiar comic book narratives, themes and characters to situate players in a world of participatory violence, crime and madness. In the first game, the player-as-Batman is situated in Arkham Asylum, a high-security facility for the criminally insane and supervillains that also temporarily houses a general population of prisoners from Blackgate Penitentiary. The elision of criminality and mental illness becomes amplified in the second game with the establishment of Arkham City, a combined facility that conflates asylum and prison, completely dissolving any distinction between crime and madness. We draw on Rafter’s conceptual framework of popular criminology to seriously interrogate the representation of violence, crime and madness in these games. More than simply texts offering popular explanations for crime, the games directly implicate the player in violence enacted upon the bodies of criminals and patients alike. Violence is necessary to move the action of the game forward and evokes a range of emotional responses from players who draw from personal experience and other cultural and media representations as they navigate the game. We argue that while the game celebrates violence and the brutal conditions of incarceration, it also offers possibilities for subversive and critical readings. While working to affirm assumptions about crime and mental illness, the game also provides a visceral and visual critique of excessive punishment by the state as a source of injustice for those deemed mad or bad.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".