Spreadable Jams: Implementing Social Scholarship through Remodeled Game Jam Paradigms
Bibliographic record
Abstract
Game design communities often come together during ‘Game Jams,’ open social events in which game makers creatively respond to a design provocation by generating numerous prototypes over a short period of time. Such prototypes are often discussed between participants at the end of the event, then shared with a larger public audience. This fruitful process, which encourages collaborative sharing rather than competition between participants, differs in purpose, structure and outcome from many existing models of academic scholarship and scholarly communication. Inspired by the potential of such events, this paper argues that the game jam paradigm, and more generally, ‘spaces apart,’ can be effectively adopted, adapted, and repurposed to facilitate a broader social generation and dissemination of research creation prototypes, modelling an alternative kind of scholarly making in the humanities that runs parallel to, and is as equally valued and valid as, existing publication models.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".