THE ENGINE IS THE MESSAGE: VIDEOGAME INFRASTRUCTURE AND THE FUTURE OF DIGITAL PLATFORMS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it