Bibliographic record
Abstract
Natural language, which is closely linked to thought and reasoning, has been recognized as important to the design process. However, there is little work specifically on understanding the use of language as design stimuli. This paper presents the results of an experiment where verbal protocols were used to elicit information on how designers used semantic stimuli presented as words related to the problem during concept generation. We examined stimulus use at the word level with respect to part-of-speech classes, e.g., verbs, nouns and noun modifiers, and also how stimuli syntactically relate to other words and phrases that represent ideas produced by the participant. While all stimuli were provided in verb form, we found that participants often used stimuli in noun form, but that more new ideas were introduced while using stimuli as verbs and noun modifiers. Frequent use of stimuli in noun form appears to confirm that people tend to think in terms of objects. However, noun use of stimuli introduced fewer new ideas and therefore contributed less to concept formation in our study. This work highlights a possible gap between how people may tend to think, e.g., in terms of nouns, and how new ideas may be more frequently introduced e.g., through verbs and noun modifiers. Addressing this gap may enable development of a language-based concept generation support system to encourage innovative and creative solutions for engineering problems.
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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 | 0.018 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".