Quantifying Effects of Oppositely and Similarly Related Semantic Stimuli on Design Concept Creativity
Bibliographic record
Abstract
Creativity is important in the design and manufacture of successful products, yet neither creativity nor the early stages of design are well understood. This lack of understanding limits the tools that can be developed to support the crucial earlier stages of design that ultimately determine product success.\n\tMy research aims to better understand creativity by studying and quantifying the potential of semantic stimuli (words) presented during concept generation. Natural language was chosen as design stimuli because language provides a systematic framework for stimuli generation. Furthermore, natural language is ubiquitous and intimately related to cognitive functions required in design such as reasoning and memory. Ultimately, the results of this research will assist in the development of early-design support tools.\n\tIn a series of four experiments, the effects of semantic stimuli oppositely and similarly related to the experiment problem were examined with respect to creativity and designers’ language patterns. Results show that opposite-stimulus concepts were significantly more creative than similar-stimulus concepts. It also was observed that opposite stimuli elicited designer behaviours that may encourage creative concepts. These results suggest that the use of oppositely related stimulus words is a practical method for encouraging creative design.
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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 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".