Effective Analogical Transfer Using Biological Descriptions Retrieved With Functional and Biologically Meaningful Keywords
Bibliographic record
Abstract
While biology is well recognized as a good source of analogies for engineering design, the steps of 1) retrieving relevant analogies and 2) applying these analogies are not trivial. Our recent work translated the functional terms of the Functional Basis into biologically meaningful keywords that can help engineers search for and retrieve relevant biological phenomena for design, addressing step 1 above. This paper reports progress towards step 2: identifying and overcoming obstacles to effective analogical transfer and application of biological descriptions retrieved with functional and biologically meaningful keywords. This work revealed that the presence of, and ease of recognizing, causal relations (relationships between two actions where one causes another) in biological descriptions plays a key role in the quality of analogical transfers. We observed that novice designers found it difficult to correctly transfer analogies when they could not easily recognize the causal relations present in biological descriptions. Two major factors that rendered this recognition difficult were: 1) a large number of action words appearing in the descriptions, and 2) key action words being used in the passive voice. To overcome these factors, we propose a template that guides designers to 1) recognize the relevant causal relations in biological descriptions and 2) focus on the functional elements of the causal relations.
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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 | 0.005 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".