Bibliographic record
Abstract
This article seeks to develop an approach to independent video game production through a synthesis of recent work in assemblage theory and critical political economy. As an alternative to the (still important and useful) Dyer-Witheford & de Peuter's immaterial-labour oriented study in Games of Empire (2009), I propose studying videogames through their historically and materially specific context, thinking about videogame development communities as assemblages (DeLanda, 2006). The assemblage of videogame production should not be conceptualized as an object over determined by global capital's immanence towards new forms of exploitation. Rather it is negotiating its way through capital, state bureaucracies, aesthetics, ad hoc decision making and the flows of bodies through urban spaces. Using interviews and data collected concerning the development of Toronto made iPad and iPhone game Superbrothers: Sword & Sworcery EP, I show how work of videogame production is both immaterial and expressive, as much as it is firmly grounded in existing material relationships to a panoply of objects. This paper then has two goals: 1) to illustrate an ontology and method of political economy and 2) contribute to the growing scholarship on indie games in the field of Game Studies.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".