Bibliographic record
Abstract
It is now widely accepted that videogames are a cultural form, and that they generate cultural meaning through the possibilities and constraints through which they shape players’ experiences and choices. However, the cultural processes through which videogames are themselves produced remain understudied and too-straightforwardly imagined. The videogame maker does not simply conceive of a videogame idea and then execute it. Instead, the videogame is produced through processes of negotiation and iteration between videogame maker, software and hardware environments and the broader expectations of the field. In this sense, videogame production can be fruitfully understood through the lens of craft. I argue that in order to politicise agency in digital play, as is this special issue’s goal, videogame research must also consider the agency of the videogame maker, and the iterative, embodied, and social processes through which videogames are produced. This article draws from interviews with videogame makers and existing research on craft production to provide a preliminary consideration of how the agency of the videogame maker as a cultural producer can be accounted for.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".