Generating gameworlds with computers: the case for procedural creativity
Bibliographic record
Abstract
Purpose Despite the popularity of the Computational Thinking (CT) paradigm and the call for broad social diffusion of CS fundamentals, the authors argue that the concept is inherently limited and limiting and does not sufficiently convey an understanding of how to enable people to create with computational technologies. The authors suggest an alternate paradigm, procedural creativity, that calls for the development of conceptual creative spaces governed by procedurally generative principles. The authors also call for game development to be the focus of procedural creativity pedagogy. Design/methodology/approach The authors first discuss the limitations of the CT paradigm, focusing, in particular, on the issue of abstraction and representation as opposed to execution and action. The authors then define procedural creativity in more detail. Following that, they discuss the use of game development as pedagogy, with a focus on Margaret Boden’s notion of conceptual creative spaces. Findings CT is limited because it focuses overly on solutions to computational “problems”, because it is tied too closely with economic concerns and because it focuses on abstraction at the cost of action. Procedural creativity, on the other hand, focuses on the individual’s capacity for personal expression with the computer and on the generative capacity of code in action. Game development is in ideal platform for procedural creativity because it emphasizes the development of creative domains and conceptual spaces. Originality/value This paper offers a challenge to the CT status quo and presents a novel way forward for understanding computation as a creative practice.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.029 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".