Bibliographic record
Abstract
Current design processes are all generally similar in approach, but none yet address sustainability directly. When sustainable design is pursued, bio-inspired design or biomimicry are invariably used. These approaches attempt to reuse principles found in nature to conceptualize artifact designs. However, sustainable design approaches have yet to be integrated into typical design processes as they occur in practice; these methods generally work by seeking solutions only on a case-by-case basis. As a result, sustainable design methods like biomimicry tend to be used as ancillary or "after the fact" techniques. To fully integrate sustainability into design processes, the authors believe we must look more deeply at the processes that occur in nature and that have led to the organisms often referred to in biomimicry and related methods. The authors’ study of natural evolutionary processes has led them to believe that there is significant similarity in the way successive generations of artifacts and organisms change. We found that we could describe processes in generic terms that applied equally well to natural evolution and the way that artifacts change over time: that is to say, we found an analogy between generational changes in artifacts and in organisms. The authors are now delving into more detail, and are finding that the analogy can extend to cover natural selection, mutation, and genetic structures. A detailed explanation of this analogy, working at several different levels, is given in this paper. Furthermore, natural and artificial lifecycle processes are shown to be nearly identical. Since the analogy appears to hold well at many levels of detail, the authors propose to create a genetic structure (a genome) for artifacts. We contend that such a structure would be useful for designers. For example, we find that using the structure of the analogy as a guide can help reduce complexity of design problems. Also, we show that the artificial “genes” lend themselves to description via pattern languages, which are also known to reduce problem complexity. We hypothesize that using pattern languages to represent an artificial genome for designed artifacts will result in a useful, more holistic approach to sustainable design - one that is literally inspired by nature.
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.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".