Engineering Safety and Risk Management: A Structured Case Study Approach to Investigating Chemical Process Safety
Bibliographic record
Abstract
Abstract Abstract Engineering leadership and management consider the organizational aspects of the development and operation of complex designs in a sustainable manner. Safety and risk management are key elements in sustainable design, operation, and management of engineering projects (Crowl & Louvar, 2019). Recognizing that a safety culture does not develop on its own, but is a product of management’s intent and consistent reinforcement (Fleming et al., 2018; IAEA, 1986), case studies help students to understand and reflect on the leadership values and management beliefs that can lead to sustainability, inherently safer designs, and a supportive organizational culture. Using case studies to connect incident stories to engineering safety, culture, and risk management can help students examine the enacted values, underlying values, assumptions, and beliefs that may contribute to major incidents (Guldenmund, 2000; Kerin, 2018; Shallcross, 2013b). Further, the education of engineers as empowered leaders who understand the implications of their own underlying values, assumptions, and beliefs and their subsequent connection to the sustainable design and operation of complex systems enhances societal sustainability. This paper proposes a case study analysis structure developed to connect the role of the underlying values, ethics, assumptions, and beliefs of people who lead, manage, and work in complex engineering projects towards the enactment of a sustainability culture or a safety culture or both. The proposed case study structure reinforces engineering education outcomes, the United Nations sustainable development goals, and Risk Based Process Safety (RBPS) management in order to further develop technical and professional skills in undergraduate and graduate students better preparing them for their future roles in a world demanding sustainable solutions.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".