GAMES OF EMPIRE TEN YEARS ON
Bibliographic record
Abstract
The panel will retrospectively evaluate the significance of the seminal text Games of Empire (2009) for new media and game studies, reflecting on the contribution of autonomous Marxism to the study of digital culture today, as well as the methodological move it performed in tracing continuities and discontinuities between sites of production and play. Each paper will take one or two key concepts from the original book, including Empire, multitude, ideology, and cognitive capitalism, and apply them to the contemporary moment in the games sector. Our aim is to explore the strengths and limitations of these concepts, as well as identify the salient ways in which the sector has evolved over the last ten years. For example, we will examine efforts at unionisation in the sector; how gender and race have emerged as key concerns in the last few years in sites of game work; how apps are affecting the representation of capitalist and military systems; and how ‘multitude’ in the sector has assumed new forms in the wake of new distribution platforms. The panel will make a case for integrating social theory with the analysis of production cultures and textual practices, as well as situating the analysis of games within the field of new media and internet studies more broadly.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".