The Creative Space Theory as a map to explore the mind
Bibliographic record
Abstract
Despite significant transformations in most domains of activities, there might still be some constancies in the creative spaces explored throughout history. This paper introduces the Creative Space Theory (CST), a conceptual framework delineating 10 distinct creative spaces, analogous to creative landscapes. These creative spaces are proposed as navigational terrains for an array of media, tools, activities, and domains. The 10 spaces of the theory are movement, sound, image, sensation, emotion, strategy, story, symbol, network, and system. Notably, these creative spaces transcend specific media, and cover artistic as well as intellectual domains. For example, the sound space would be relevant to music, poetry, filmmaking, and acting among others, whereas the system space may be relevant to engineering, medicine, science, and design among others. The proposed theory holds potential utility in three key areas: (1) nurturing individual’s creative potential, (2) helping creators adapt to continuously changing circumstances, and (3) fostering positive creative self-beliefs in overlooked domains of creation. The current paper is a theoretical elaboration. We describe the creative spaces and discuss the implications of the theory towards individuals, educational practices, and research within the fields of cognition and Artificial Intelligence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".