Rationalizing Play: A Critical Theory of Digital Gaming
Bibliographic record
Abstract
This article constructs a new framework for the study of games as sites of social rationalization, applying Feenberg's critical theory of technology. We begin by making the case for a consideration of games as systems of social rationality, akin to other modern systems such as capitalist markets and bureaucratic organizations. We then present a conceptualization of play as a process through which the player focuses attention away from the undifferentiated action of everyday life toward a differentiated sphere of playful activity. This approach reveals how the experience of play changes as it becomes rationalized through the technological mediation and widespread standardization that occurs as games become large-scale social practices. We propose a theory of the rationalization of play (ludification), which outlines the key components of socially rationalized games, which we then apply to the specific example of massively multiplayer online games (MMOGs).
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".