Design Is How We Change the World! Can We Do It in Socially, Environmentally and Economically Acceptable Ways? Synthesizing Design Tools for This Utopian Concept
Bibliographic record
Abstract
Since the beginning, humans advanced their civilization by making better tools to improve their lives. Tools and products were designed for better living considering manufacturing issues, cost and time as predominant criteria. It has become clear that not considering environment and society, both at local/global levels, has now become a major impediment affecting living conditions on a large portion of the Earth and in many societies. Design methodologies should lead to creative solutions with consideration to engineering and economics for practicality but also to environmental and social constraints for sustainability. We propose a comprehensive design methodology based on multidisciplinary design to include the knowledge of humanities, environmentalists, science and engineering, and allowing for experts’ inputs from these areas to provide a holistic approach to engineering design . For example, experts in humanities are expected to interact with stakeholders to evaluate their value systems to provide guidance for the design. The methodology that we synthesize is new and combines (i) Societal level impacts at all scales, (ii) Environmental impacts and (iii) Engineering design with economic impacts, including uncertainty considerations. The proposed design methodology is called Social-Environmental-Economical-Engineering Framework (SEEEF). It can utilize concepts and tools such as Circular Design, Doughnut Economics, design based on environmental life cycle analysis, among others. SEEEF is quantity based and provides steps for evaluating any project or product in an objective manner and will help train engineers in design for sustainability. It also provides non-engineers with a significant role in design to increase their understanding of the hard constraints of engineering. Ultimately, SEEEF allows society to take an informed decision considering short/long term and local/global impacts of the design and the pertinent uncertainties.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".