“How Do We Play this Thing?”: The State of Historical Research on Videogames
Bibliographic record
Abstract
Abstract Though previously overlooked by academia, scholars from a wide array of fields now consider videogames as a serious subject of inquiry. The emergence of game studies as a standalone discipline has led to the publication of high-quality work on the medium, yet the field of videogame history is still immature. Initial attempts to introduce critical historical analysis of videogames in a field dominated by journalistic accounts were themselves plagued by an overemphasis on videogame canons and on the United States and Japan. In effect, early writings by videogame historians resembled “great man” theory, something one could qualify as “great game” theory. Over the last decade, this situation has started to be redressed and there are now growing efforts to produce solid historical scholarship on videogames. Still, game scholars and game historians need to collaborate, engage in conversation, and develop and adapt proper methods to conduct historical research on videogames in order to write relevant histories of this relatively young medium.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".