Playing at apocalypse: Reading Plague Inc. in pandemic culture
Bibliographic record
Abstract
Plague Inc. is an enduringly popular mobile video game in which players create diseases and attempt to eradicate humanity; it has been downloaded more than 60 million times and been met with largely positive critical reception, with many reviews praising the game as a ‘realistic outbreak simulator’. This article explores Plague Inc. as both an artifact, and productive, of ‘pandemic culture’, a social imaginary that describes how the threat of pandemic increasingly shapes our day-to-day life. Ludic and narrative elements of the game were identified and selected for analysis, along with paratexts surrounding the game. Three aspects of Plague Inc. were used to structure the analysis: its politics of global scale, its viral realism, and its visual culture of contagion. The article examines how the ways in which Plague Inc. articulates ideas about pandemic may not only explain the game’s immense success but also provide insights into public perceptions and popular discourses about disease threats. The article argues that the game is an incomplete text that depends on preexisting familiarity with other disease media. It concludes that the popularity and longevity of Plague Inc., as well as its broader social relevance, can be explained by placing it within the context of public anxieties about vulnerability to infectious diseases.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".