Accelerating Climate Change Adaptation (CCA) Education for the Next Generation of Engineering Professionals
Bibliographic record
Abstract
Climate change adaptation knowledge is not yet easily accessible to students in professional programs in Canada. In part, this stems from stringent degree requirements that follow competencies regulated by professional bodies. It may be exacerbated by factors including the interdisciplinary nature of climate change, organizational inertia and disciplinary silos. This paper is based on a project funded by Natural Resources Canada on “Accelerating Climate Change Education for the Next Generation of Professionals”, with an emphasis on climate change adaptation (CCA) skills in Civil and Environmental Engineering, specifically. The project enables action by developing needs-based curriculum and pedagogy that draws on interdisciplinary expertise, and making these frameworks and associated tools available to post-secondary institutions and professional associations Canada-wide through a community of practice. The project research goal is to describe current practices and identify gaps or opportunities for the integration of CCA education into existing Canadian engineering, planning, architecture and accounting programs. The current paper is focused on engineering curricula, with the engineering scope limited to civil and environmental engineering, which at the University of Waterloo and many universities in Canada represents the fields focussed on civil infrastructure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".