Bibliographic record
Abstract
The relationship between language and reasoning motivates us to study the use of language within engineering design. This paper describes our continued investigation of language as stimuli for concept generation. Specifically we investigate dichotomous lexical stimuli that are related to the problem in either a disagreeing, incongruent manner or in an agreeing, congruent manner. This is a follow-up investigation where we extend previous experiments to include both congruent and incongruent stimuli to enable comparison of differences between designer behavior and concepts. A between-subjects think-aloud experiment was performed where participants were presented with a problem and asked to generate concepts to address the problem. Half the participants were provided with incongruent stimuli and the remaining were provided with congruent stimuli. Participants provided with incongruent stimuli used the stimulus words as verbs more often than the participants provided with congruent stimuli. Verbs possess several properties desirable for use as design stimuli including the increased introduction of new lexicalized concepts to the concept generation process. When two independent raters scored the concepts, there was a positive correlation between the raters that concepts developed with incongruent stimuli were more novel. Understanding the effects of different lexical stimulus types on concept generation contributes to the development of design support tools that exploit the relationship between language and reasoning to increase design novelty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".