Game Studies meets Surveillance Studies at the Edge of Digital Culture: An Introduction to a special issue on Surveillance, Games and Play
Bibliographic record
Abstract
While we could attribute the close ties between surveillance and video games to their shared military roots, in this editorial we argue that the relationship goes much deeper to that. Even non-digital games such as chess require a mode of watchfulness: an attention to each piece in relation to the past, present, and future; a drive to predict an opponent’s movements; and, a distillation of the player-subject into a knowable finite range of possible actions defined by the rules. Games are social sorting, disciplinary, social control machines.In this introduction we tease apart some of the intersections of games and surveillance, beginning with a discussion of the NSA documents leaked by Edward Snowden on using games to both monitor and influence unsuspecting populations. Next, we provide an overview of corporate data-gathering practices in games and further outline the production of manageable, computable subjectivities. Then, we show how the game Watch Dogs explores the surveillant capacities of games at both the game mechanical and representational scales. These three different facets of surveillance, games, and play set the scene for the special issue and the diverse articles that follow. In the following pages we pose new lines of questioning that highlight the nuances of play and offer new modes of thinking about what games - and the processes of watching and being watched that are a foundational part of the experience – can tell us about surveillance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".