Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it