Playful Lenses: Using Twine to Facilitate Open Social Scholarship through Game-based Inquiry, Research, and Scholarly Communication
Bibliographic record
Abstract
In academic contexts, digital games are often studied as texts or are used as pedagogical tools to teach basic concepts in early education situations. Less usefully, their systems and economies are often co-opted and decontextualized in short-sighted attempts to “gamify” various aspects of learning or training. However, given that games are highly controlled, conditional, choice-and-consequence-based, problem-solving environments in which players are expected to interact with simulated settings and elements after agreeing to take on particular roles and subject positions, there are promising potential uses of these experiences in academic contexts that have not been fully considered. Motivated by the imperative to explore alternative modes and methods of scholarly research and communication, and guided by the values of open social scholarship practices, this paper reconsiders games not as things to study, but as instruments to study with. Given that games can function as simulations, models, arguments and creative collaboratories, game-based inquiry can be used as a potential method of post-secondary and post-graduate humanities research and scholarly communication. While these ideas have been explored in a preliminary way in relation to a number of different academic disciplines (Donchin 1995; Boot 2015; Mitgutsch and Weise 2011; Westecott 2011) this paper is meant to catalyse a humanities-calibrated consideration of the pragmatics and potentials of game-based research, games as instances of critical making and scholarly communication, and more complex forms of game-based learning than those currently practiced. A number of examples that make use of the open source Twine platform will be featured.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".