Bibliographic record
Abstract
This work aims to improve creativity and innovation in design by facilitating the use of cross-domain analogies, particularly from biological phenomena, as stimulus for concept generation. Rather than create an enormous database of biological knowledge to specifically support engineering design, we have chosen to take advantage of the large amount of biological knowledge already in natural-language format, e.g., books, journals, etc. Relevant biological analogies for any given design problem are found by searching for instances of functional keywords that describe the intended effect of the design solution in a natural-language corpus. However, the optimal choice of keywords, or search terms, is complicated by the fact that engineers and biologists may use differing domain-specific lexicons to describe related concepts. Therefore, an engineer without sufficient background in biology may not be able to identify keywords with biological connotation that are not obviously related to the engineering keywords. This paper describes efforts to bridge the gap in lexicons by examining words that frequently collocate with searched words. The biological meaningfulness of these bridge words is characterized by how frequently they occur within definitions of biological terms in a biology dictionary. Search words identified this way may not be obvious to domain novices, and may parallel those suggested by domain experts, thus facilitating the use of cross-domain ideas to support design. Our approach of generating bridge words with biological meaningfulness is generic and can be used to bridge any disparate domains (e.g., engineering and economics). Thus designers are enabled to quickly access relevant concepts from different domains to produce more innovative solutions.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".