Struggling with Simulations: Decoding the Neoliberal Politics of Digital Games
Bibliographic record
Abstract
As a creative industry currently rivalling film and television, digital games are filled with a variety of political tensions that exist both between and within particular works. Unfortunately, internal discrepancies are often dismissed as indicators of political ambivalence, or treated as formal flaws that need to be overcome. To address this gap, this dissertation draws from game studies, media studies, and political economics to investigate the contradictory relationships between popular games and neoliberalism, specifically in relation to playful forms of resistance and critique that emerge during gameplay. Part I develops this study’s methodology by drawing from corresponding uses of assemblage theory, specifically articulated in Ong (2006, 2007), Lazzarato (2012, 2015), and Gilbert’s (2013) control society approaches to neoliberalism and Taylor (2009), Pearce and Artemesia’s (2009) digital ethnographic approaches to play. Derived from the French agencement, assemblage theory emphasizes heterogeneous relations in constant states of becoming that are understood as being real. Part II implements the aforementioned methodology by examining some of the most popular gaming franchises produced to date, with each demonstrating emergent political correlations and dissonances springing from relationships between different ludic and narrative components. BioShock (2007–2013) and Red Dead Redemption (2011) are narratively structured by neoliberal discourse, yet each storyline fails to correspond with the resistant political logic embedded in their respective rule systems. Conversely, Call of Duty (2004 – present) attains a high level of political cohesion that does not result in a better playing experience, as much as it contributes to conflicts amongst publishers, developers, and fans. Finally, Minecraft (2009–present) provides a fascinating example of a game that representationally reinforces neoliberalism while simultaneously affording the creation of new digital objects, including objects that give players the opportunity to understand and appreciate the computational infrastructures that a neoliberal emphasis on source code takes for granted. This dissertation, as a result, charts the growing connections between emergent gameplay and new forms of resistance and critique—connections that contribute not only to game studies, but also to the study of digital media and the interdisciplinary study of neoliberalism.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".