Ludic Epistemology: What Game-Based Learning Can Teach Curriculum Studies
Bibliographic record
Abstract
"Ludic epistemology" references the need for educational game studies to remediate traditional (linguistically mediated) epistemologies. Its guiding questions are about what it means to encode knowledge in the form of a game, and how we might conceive coming to know as a process of playing. In digital game studies, a theory of ludic epistemology is concerned with the distinctive demands of-and the particular constraints upon knowledge representation in the development of computer-supported game-based learning environments. Its primary theoretical questions are about the re-mediation of educational knowledge and its representation. What educational game studies does for curriculum is to radically stir things up. Its core theoretical project of formulating a "ludic epistemology" can advance epistemic inquiries into media and learning, and respond to what have become serious questions for educators about how game-based technologies for learning, and emergent digital epistemologies, reform and re-forge relations between learning and play.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".