Climate change in Canadian civil engineering curricula: gaps and best practices
Bibliographic record
Abstract
This study examines the state of curricula, knowledge, and practices in Canada’s civil engineering programs with respect to climate change, identifies best practices, and formulates recommendations on ways to improve climate change knowledge and skills training for Canadian civil engineering programs. A survey based on the Climate Framework was distributed to 856 civil engineering instructors across all 25 accredited engineering schools listed by Engineers Canada and gathered data on 55 civil engineering climate change related courses, with representation from 20 out of the 25 accredited programs across the country. The results of this study provide important benchmarks to help instructors incorporate most relevant climate change topics and resources into their courses for equipping civil engineering students with the necessary skills to tackle emerging challenges related to the mitigation of climate change and adaptation to wildfires, droughts, sea level rise, increased precipitation, storms, heat domes, floods, earthquakes, and biodiversity loss.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".