Bibliographic record
Abstract
The precarious reality of videogame production beyond the corporate blockbuster studios of North America. The videogame industry, we're invariably told, is a multibillion-dollar, high-tech business conducted by large corporations in North America, Europe, and East Asia. But, in reality, most videogames today are made by small clusters of people working on shoestring budgets, relying on existing, freely available software platforms, and hoping, often in vain, to rise to stardom—in short, people working like artists. Aiming squarely at this disconnect between perception and reality, The Videogame Industry Does Not Exist presents a more accurate and nuanced picture of how the vast majority of videogame-makers work. Drawing on insights from over 400 game developers, Brendan Keogh develops a new framework for understanding videogame production as a cultural field in all its complexity. Part-time hobbyists, aspirational students, client-facing contractors, struggling independents, artist collectives, and tightly knit local scenes—all have a place within this model. But proponents of non-commercial game-making don't exist in isolation; Keogh shows how they and their commercial counterparts are deeply interconnected and codependent in the field of videogame production. A cultural intervention, The Videogame Industry Does Not Exist challenges core assumptions about videogame production and reveals the diverse and precarious communities, identities, and approaches that make it a significant cultural practice.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".