Comparative Evaluation of a Novel Concept Design Method
Bibliographic record
Abstract
Based on their previous work in creating a new method of design, termed the “Design by DNA” method, the authors are now experimentally validating the method against other, known methods. The goal of the experiment is to determine if Design by DNA promotes creative designs. Specifically, the authors are seeking to measure and compare creativity resulting from the use of Design by DNA and from other, known design methods. However, few have conducted empirical experiments in the past, and further, the literature on comparatively evaluating creativity of different design methods is relatively sparse. Therefore, the authors are developing a framework for defining and executing meaningful experiments that can accommodate various design methods, including Design by DNA, and also provide meaningful data to comparatively evaluate those methods, with the goal of determining whether Design by DNA impacts creativity in design. The experimental framework is described, and results of a pilot experiment are given. In that framework, creativity was characterized by novelty, usefulness, and cohesion. Due to small sample sizes, confidence in the results is not particularly high. Even so, some results do indicate several points of interest. An analysis of the results suggests that Design by DNA can offer advantages in engineering design, ranking higher in both the ‘usefulness’ and ‘cohesion’ categories of the creativity assessment. Hypotheses are given to explain why the experimental results show the slightly lower score in the ‘novelty’ category. Experiment participants were also evaluated on the NASA Task Loading Index (TLX) to evaluate how taxed they were using the different design methods and results are shown. Here, the Design by DNA method accrues better scores in 5 of the 6 NASA TLX categories, suggesting that it was less strenuous on the participants than the other methods. Statistical analysis of both the creativity scores and the TLX document shows confidence levels of between 65% and 96%, which is acceptable for very low populations. As this was a pilot experiment, the authors foresee future work to improve the results presented here. First, larger sample sizes are needed to improve statistical significance of our conclusions. Secondly, the authors wish to set out a series of experiments whereby each test is run by pitting one specific design method against the Design by DNA method, to better show a 1-on-1 comparison between the methods and highlight the strengths and weaknesses of each.
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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 | 0.042 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".