Bibliographic record
Abstract
Sustainability is the capacity to enable the co-existence of both earth's biosphere and human civilization.The basic premise of sustainability is that the earth's resources cannot be used, depleted, or damaged indefinitely (Portney 2015).Sustainability concerns with three major domains: environment, society, and economy (EPA 2015).Sub-domains of sustainability are cultural, technological, and political (James et al. 2014;Magee et al. 2013).The current strategies for achieving sustainability can generally be divided into three categories: population, affluence, and technology (Holdren, J. P., & Ehrlich 1974).The most promising path to sustainability is to develop new technologies that address the relationships among the environment, society, and economy, including the design, making, transporting, use, and disposal of the product.The foremost important task in sustainability is to conduct a holistic analysis of all factors concerned with the environment, society, and economy related to a product.The first paper, authored by Yang et al. and titled "Environment-Based Life Cycle Decomposition (eLCD): Adaptation of EBD to Sustainable Design", proposes a life cycle analysis tool named Environment-based life cycle decomposition (eLCD) to support the sustainable design during the conceptual design stage.In contrast to the existing analysis tools, which focus on the life cycle assessment of a product after the product is already designed or even made, eLCD helps designers effectively and efficiently collect information for sustainability related decisionmaking.In achieving the objective, eLCD decomposes the sustainability environment into three dimensions: environment types, life cycle events, and life cycle time.The environment types are designated as natural, built (including physical artifact and digital artifact), economic, and social environment.It is this holistic environment structure for sustainable conceptual design that makes sustainability is an integral part rather than a burden of the design.The effectiveness of eLCD is demonstrated by its application to the upscaling of a wind turbine, where an energy storage system is introduced to make full use of wind energy with the least waste in serving the electricity demand.The second paper, authored by Lee et al. and titled "Design Thinking with Appropriate Technology for Improving Social Sustainability: Critical and Comprehensive Criteria", develops criteria for integrating design thinking processes with appropriate technologies for improving social sustainability.The authors argue that current technology promotions are empathetically disconnected from local people and culture due to the lack of affordability, maintenance, and personal training.Five assumptions are proposed for promoting design thinking in the processes: 1) user-oriented design with mass productivity; 2) reiterative nature through user satisfaction surveys; 3) affordability for purchase, maintenance, and repair services; 4) local appropriateness; and 5) eco-friendliness with environmental sustainability.They applied the criteria to 28 representative cases from water, energy, health, shelter, and transportation fields.The cases were quantitatively and qualitatively evaluated using a comprehensive literature survey on the use of appropriate
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".