Bibliographic record
Abstract
This abstract is a report on a qualitative meta-analysis of the methods used in choosing games for study. Game Studies continues to develop as a discipline just as digital games continue to evolve. While there remains an interest in examinations of specific games for various purposes, as the number and sophistication of titles released in a given year continues to rise, it becomes necessary to closely at how we are choosing the games we study, the criteria we use for those studies, and how we support our claims about the suitability of the game for our purposes. Often, studies of individual games are conducted with the hopes of being able to generalize at least some of the conclusions to other games and/or other players. Given the number and variety of games with no cleanly defined delineations of genre, can it be assumed that it is possible to examine one game and make generalizations to other games? Games are no longer trivial, nor frivolous so this is not a straightforward question. Claims that a particular game meets certain criteria critical to the analysis should be supported by something beyond the author's say-so. As studies on, with, and of games become more accepted and common in mainstream educational research, it will also become more important to justify the choices of subjects. This has not been common practice to date.
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 | 0.626 | 0.800 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".