Integration of Climate Change in the Analysis and Design of Engineered Systems: Barriers and Opportunities for Engineering Education
Bibliographic record
Abstract
The goal of this paper is to begin mapping perspectives of engineering faculty on barriers and opportunities related to the integration of climate change in the analysis and design of engineered systems (CC&ES). Although both sustainability and renewable energy have been receiving increasing attention in engineering education for quite some time, climate change, especially as it relates to engineered systems, has yet to become a widely accepted topic of teaching and research. From recent literature on engineering education and from interviews with engineering faculty, a picture emerges of whether and how climate change is an important dimension in the analysis and design of engineered systems. From those sources, we begin to see what it might take to incorporate the relationship between climate change and engineered systems in engineering education, what the barriers and opportunities to this incorporation might be, and what strategies might be available to institutionalize this incorporation in engineering education. Support for this paper comes from a larger research project on “Climate Change, Engineered Systems, and Society” which has the goal to develop conceptual and educational frameworks and networks of change agents to promote effective formal and informal education for engineering students, policymakers and the public at large. The project partners include the National Academy of Engineering (NAE), Arizona State University, Boston Museum of Science, Colorado School of Mines (CSM), and the University of Virginia. Within this larger team, the CSM team is planning to develop a testbed for the incorporation of CC&ES in engineering education. Hence, our first step is to find related curricular innovations in the engineering education literature and perspectives from engineering faculty on barriers and opportunities to the integration of CC&ES in engineering education.
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".