Videogame Production: How the Capitalist Socius and Platformization Subjectivate
Bibliographic record
Abstract
Diverse representations of bodies in videogames has become a point of contention among developers and consumers alike, which has lead scholars to question why videogame production is breaking with trends of recognizable, anthropocentric characters in favor of “diverse” bodies. This paper contends that the overarching reason for this is that the capitalist socius (Deluze and Guattari, 1986) has become more readily equipped to be able to monetize and streamline diversity away from being an act of subversion and into an easily manipulatable source of revenue. In examining how the capitalist socius overlays onto the videogame production process, a few things become apparent. Because videogame production operates within the capitalist socius, their goals are the similar: to become autopoietic (able to reach a point of homeostasis in which the entity is able to reproduce and maintain its structural integrity) and to turn any and all resources into sources of capital generation. The expectation of bodies working in these regimes is to be as non-threatening and as pliable to new modes of subjectivation and capital generation as possible, but that means that bodies must undergo certain political transformations to adhere to these needs of the capitalist socius and videogame production process. As with any hegemonic structure, there are pockets of resistance that look to buck the current trends of subjectivation and capital generation. The form of resistance this paper examines is personal-games and affective experiences, but as with most things pertaining to the capitalist socius, personal-games are dangerously close to being swept up, monetized, and crunched down.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".