Bibliographic record
Abstract
This paper examines the use of language, specifically verbs, as stimuli for concept generation. Because language has been shown to be important to the reasoning process in general as well as to specific reasoning processes that are central to the design process, we are investigating the relationship between language and conceptual design. The use of language to facilitate different stages of the design process has been investigated in the past. Our previous work, and the work of others, showed that ideas produced can be expressed through related hierarchical lexical relationships, so we investigated the use of verbs within these hierarchical relationships as stimuli for ideas. Participants were provided with four problems and related verb stimuli, and asked to develop concepts using the stimuli provided. The stimuli sets were generated by exploring verb hierarchies based on functional words from the problem statements. We found that participants were most successful when using lower level (more specific) verbs as stimuli, and often higher level general verbs were only used successfully in conjunction with lower level verbs. We also observed that intransitive verbs (verbs that cannot take a direct object) were less likely to be used successfully in the development of concepts. Overall, we found that the verb chosen as stimulus by the participant directly affects the success and the type of concept developed.
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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 | 0.008 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".