Hypermodern Video Games as Emblems of Empire or How the Gaming Multitude Adapts to Hypermodernity
Bibliographic record
Abstract
To demonstrate how Games of Empire (Dyer-Witheford, N., & de Peuter, G. (2009). Games of empire. Global capitalism and video games, Minneapolis: University of Minnesota) elaborated an important standpoint within critical game studies, this article discusses the thesis that a specific type of video games perfectly converges with our contemporary modes of representation and praxis, which are best situated within the paradigm of hypermodernity (Lipovetsky, G. (1983). L’ère du vide: Essais sur l’individualisme contemporain. Paris. Gallimard, coll. «Folio essais»; Lipovetsky, G. & Charles, S. (2004). Les temps hypermodernes. Paris: Bernard Grasset, «Nouveau collège de philosophie»). Hypermodernity radicalizes modernity because, within hypermodernity, values such as progress, reason, and happiness are overly ( hyper) actualized rather than surpassed ( post) (Aubert, N. (2006) (dir). L’individu hypermoderne. Toulouse: Eres, coll. «Sociologie clinique»; Giddens, A. (1990). The consequences of modernity. Stanford: Stanford University Press). Based on an archetypal account, that is, a theoretical model rather than a case study, this article will show how hypermodern video games' commercialization and use within a capitalist context are emblematic of hypermodernity. We will also evaluate how these games promote adaptation to hypermodernity toward an "ideal" becoming-player for Empire. In conclusion, if playing can be seen as the multitude's escape hatch out of the dominant order, this article will explain how hypermodern video games, as a media, may also be viewed as a key site where asymmetrical and unequal relationships replicate within Empire.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".