THE ENGINE IS THE MESSAGE: VIDEOGAME INFRASTRUCTURE AND THE FUTURE OF DIGITAL PLATFORMS
Bibliographic record
Abstract
On January 18, Microsoft revealed its $68.7 billion deal to acquire videogame publisher Activision Blizzard. The acquisition was pitched as an investment towards “metaverse platforms” that gaming would play a key role in developing. Journalists speculated about the increasing consolidation of the videogame industry and whether blockbuster franchises would be locked into Microsoft’s platforms and subscription services. Commentary on the metaverse weighed in on how toxicity and harassment in game industry workplaces such as Activision Blizzard might relate to issues of trust and safety in virtual worlds such as Meta’s Horizon Worlds. Seemingly above the fray of platform strategy, market speculation, and corporate scandal, New Yorker writer Kyle Chayka (2022) tweeted as a matter of fact: “video game infrastructure and tools are increasingly going to take over all digital platforms”. This panel contextualizes discussions about the business and aesthetics of 3D platforms in the infrastructural work of game engines, which routinely integrate databases, file formats, web protocols, and translational algorithms. We trace public debates and corporate statements over representation and governance, equity and inclusion (Bosworth 2021) to the techniques, technologies, and practices that enable massive real-time 3D digital spaces to flow and transact. We also highlight the growing intertwinement between game engine development companies and related content ecosystems, such as the Epic Games Store and the Unreal Engine, and Epic’s and Unity’s Asset Stores. This panel investigates how digital systems are designed to regulate technical interoperability and its implications for creative practice and cultural production. Together, these papers map how power and capital become centralized and distributed throughout the back end of the metaverse, and politicize how social practices and subjectivities are negotiated through technological architecture.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.025 | 0.029 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".