Bibliographic record
Abstract
Valve Corporation’s digital game distribution platform, Steam, is the largest distributor of games on personal computers, analyzed here as a site where control over the production, design and use of digital games is established. Steam creates and exercises processes and techniques such as monopolization and enclosure over creative products, online labour, and exchange among game designers. Stuart Hall’s encoding/decoding framework places communication at the centre of the political economy, here of digital commodities distributed and produced by online platforms like Steam. James Gibson’s affordance theory allows the market Steam’s owners create for its users to be cast in terms of visuality and interaction design. These theories are largely neglected in the existing literature in game studies, platform studies, and political economy, but they allow intervention in an ongoing debate concerning the ontological status of work and play as distinct, separate human activities by offering a specific focus on the political economy of visual or algorithmic communication. Three case studies then analyze Steam as a site where the slippage between game-play and work is constant and deepening. The first isolates three sales promotions on Steam as forms of work disguised as online shopping. The second is a discourse analysis of a crisis within the community of mod creators for the game Skyrim, triggered by changes implemented on Steam. The third case study critiques Valve Corporation’s positioning of Steam as a new space to extract value from play by demonstrating historical continuity with consumer monopolies. A concluding discussion argues Steam is a platform that evolves to meet distinct crises and problems in the production and circulation of its digital commodities as contradictions arise. Ultimately, Steam shows how the cycle of capital accumulation encourages monopolization and centralization.
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 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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".