Language Games/Game Languages: Examining Game Design Epistemologies Through a ‘Wittgensteinian’ Lens
Bibliographic record
Abstract
Recent theorizing around games and notions of play has drawn from a pool of mid-20th century scholars including such notables as Johann Huizinga, Gregory Bateson, Roger Caillois and Ludwig Wittgenstein. Through his articulation of the concept of language as a type of game, Wittgenstein has been both adopted and critiqued for purposes of circumscribing what are now commonly held as the necessary constituents of games including their systemic nature and the acquiescence of their participants to an agreed-upon rule structure: a set of rules which Wittgenstein likens to the ‘grammar’ of language (Salen and Zimmerman, 2001;Suits, 1978; Juul, 2005; Wittgenstein, 1953; Finch, 2001; Brenner, 1999). Although thus far Wittgenstein has served as a pillar of 20th and 21st century game theory canon, this paper adopts Wittgenstein’s notion of language-games not for purposes of examining games, but for purposes of examining the design of games. The pursuit of this paper is to utilize Wittgenstein’s lens of the language-game to investigate what it is that informs and consequently shapes and reinforces game design epistemologies in an attempt to encourage a reflexivity about the design practices behind the games we create.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.062 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".