From a conventional to a sustainable engineering design process: different shades of sustainability
Bibliographic record
Abstract
The challenge of realigning the present path of development on a sustainable trajectory concerns all sectors of society, including engineering. To move towards a sustainable practice of engineering, the design process needs to be modified in order for engineers to efficiently tackle the related issues. Such ‘sustainable design processes’ (SDPs) are proposed in the literature. By reviewing the conventional design process and SDP, this paper aims to identify the differences between both approaches. The potential contribution of recent design theories, methods and techniques to sustainable engineering is also briefly discussed. Tasks from existing SDPs are combined with crucial complements into a novel integrated sustainable engineering design process. Instead of representing conventional and sustainable engineering as a dichotomy, this paper places both paradigms on a continuum along which the engineer can position himself and assess his progress. The proposed procedure reveals shades of sustainability along six dimensions: (1) the structure of the design process, (2) the scope of sustainability issues covered, (3) the relevance of the indicators considered, (4) the accuracy of the tools used for evaluation, (5) the potential improvements expected from the alternatives assessed and (6) decision-making.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".