Creativity Paradigms and Game Design Research: A Transdisciplinary Approach
Bibliographic record
Abstract
Abstract Using a transdisciplinary approach, this paper shows how creativity studies and game design research can complement each other and open new research avenues. In order to study the role of creativity in game designers’ practice, we first touch on the epistemological foundations of creativity studies and game design research. After presenting these disciplines’ epistemological underpinnings, we conduct a qualitative content analysis of game design literature, using Vlad Petre Glăveanu’s “creativity paradigms” as our analytical framework. According to these paradigms, the results show that creative game designers are still largely depicted in the literature as either geniuses or skillful individuals, while cultural and social aspects of creativity are overlooked despite their importance in current game design practice. All in all, an epistemological reflection is necessary to bridge the gap between creativity studies and game design studies. We conclude that a pragmatist approach is a promising avenue to bring forward a comprehensive view of creativity in game design practice. Finally, in order to establish theories suited for a wide array of disciplines, this paper also advocates for more transdisciplinarity in creativity research.
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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 | 0.034 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".