The GameBling Game Jam
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
Gambling scholars may be unfamiliar with the research methods used by their colleagues in game studies. Yet, as gambling becomes gamified, and gaming becomes gamblified, the intersection between our two fields continues to grow. The GameBling game jam, which took place in 2022 at Concordia University, proposed to explore this growing intersection by applying a game making and game studies method—the game jam (see, for instance, Kultima 2015; Meriläinen et al., 2020; Ruberg & Shaw, 2017)—to a gambling object—the slot machine. This post argues that game jams can be used in gambling studies to learn more about public perceptions of slot machines, to reverse-engineer black-boxed gambling algorithms, or even to help new research interests emerge through the process of game creation. We ultimately propose that the practice of creating games from scratch in a limited time frame, or "game jamming," is an innovative research method that can help uncover new ways to think about and question social science concepts.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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