Bibliographic record
Abstract
Critical IR theory engages with popular representations of global politics, particularly films, photography, and literature (Amoore 2007; Bleiker 2001; Danchev and Lisle 2009; Shapiro 2009). Gaming plays an increasingly large role in popular culture: the launch of video games, such as Halo, Call of Duty, or Gears of War, garner as much money and attention as blockbuster films. The Entertainment Software Rating Board presents the following picture of the industry: 67% of US households play video games—40% of whom are women, with sales in $10.5B in 2009 (ESRB 2011). Global sales of $40B make it equal to other entertainment industries, such as music ($30-40B) or movies ($27B). Consequently, we must take gaming to be as much a part of the imbrication of global politics into the everyday as movies, music, or literature. The taken-for-grantedness, the unremarkability of games that use tropes and figures of global politics functions to render unproblematic the common sense of international relations. Just because these games take place in the basement, the living room, or in the rec halls of Forward Operating Bases, and not the UN Security Council chambers, does not make them any less a vital part of the construction of IR. When IR theorists invoke the “rules of the game” or game theory as a frame for systematically separating agents from structures, and the conditions of possibility for politics, they are limiting the bounds of play—ascribing a set of primarily social conventions (rules) to an abstract structure of world politics (the game). Play and these ludological tropes are as vital to our political imagination as a self-styled “serious” reading of politics. We must look at gamic representations of the world and international relations—not as decision-making tools but as artifacts of popular culture (Grayson, Davies, and Philpott 2009; Höglund 2008).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".