Safe-by-Design in Engineering: An Overview and Comparative Analysis of Engineering Disciplines
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we provide an overview of how Safe-by-Design is conceived and applied in practice in a large number of engineering disciplines. We discuss the differences, commonalities, and possibilities for mutual learning found in those practices and identify several ways of putting those disciplinary outlooks in perspective. The considered engineering disciplines in the order of historically grown technologies are construction engineering, chemical engineering, aerospace engineering, urban engineering, software engineering, bio-engineering, nano-engineering, and finally cyber space engineering. Each discipline is briefly introduced, the technology at issue is described, the relevant or dominant hazards are examined, the social challenge(s) are observed, and the relevant developments in the field are described. Within each discipline the risk management strategies, the design principles promoting safety or safety awareness, and associated methods or tools are discussed. Possible dilemmas that the designers in the discipline face are highlighted. Each discipline is concluded by discussing the opportunities and bottlenecks in addressing safety. Commonalities and differences between the engineering disciplines are investigated, specifically on the design strategies for which empirical data have been collected. We argue that Safe-by-Design is best considered as a specific elaboration of Responsible Research and Innovation, with an explicit focus on safety in relation to other important values in engineering such as well-being, sustainability, equity, and affordability. Safe-by-Design provides for an intellectual venue where social science and the humanities (SSH) collaborate on technological developments and innovation by helping to proactively incorporate safety considerations into engineering practices, while navigating between the extremes of technological optimism and disproportionate precaution. As such, Safe-by-Design is also a practical tool for policymakers and risk assessors that helps shape governance arrangements for accommodating and incentivizing safety, while fully acknowledging uncertainty.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it