Game Studies at Scale: Towards Facilitating Exploration of Game Corpora
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
Critically playing a game, and performing a close reading of a specific aspect of a game, are valid game analysis techniques. But these types of analyses don’t scale to the plethora of games available, and also neglect implementation aspects of the games which themselves are texts that can be analyzed. We argue that appropriate software tools can support research in game studies, allowing individual games to be read at the level of gameplay as well as the implementation level. Moreover, these tools permit analysis to scale in a similar fashion as distant reading allows for traditional texts, and be applied to an entire corpus of games. We illustrate these ideas using a corpus of games created using the Graphic Adventure Creator, a program first released in 1985 for a number of computing platforms. As a proof of concept, we have built a system called GrACIAS – the Graphic Adventure Creator Internal Analysis System – that we have used for both static and dynamic analysis of this corpus of games, effectively allowing them to be internally explored and “read.” Furthermore, our system is able to look for game solutions automatically and has solved over 60 game images to date, making the games accessible to researchers, but also people who may not be expert players or even able to understand the language the game uses.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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