COMPARISON OF THE CIVIL ENGINEERING CURRICULUM AMONG SEVERAL CANADIAN UNIVERSITIES
Bibliographic record
Abstract
Growing attention to environmental sustainability, modular construction, and application of new generation of materials, accompanied with advanced data collection techniques and computer modeling, has revolutionized the area of Civil Engineering within the past few years. This demonstrates the necessity of continually reviewing the curriculum to assure that graduating engineers are knowledgeable enough to deal with complex problems in their area of specialty. This is also essential to satisfy the continual improvement process (CIP) requirements mandated by the Canadian Engineering Accreditation Board (CEAB). As a first step to design a rigorous CIP, a comprehensive comparison was made between the Civil Engineering curricula of the University of Alberta (UofA) and eight other major universities across Canada, including the University of Calgary, University of Toronto, McGill University, University of Windsor, University of Regina, University of British Columbia (UBC), University of Waterloo, and Polytechnic of Montreal. After categorizing the courses into twelve different streams, it was observed that some universities paid less attention to a specific stream in comparison with the average, which could be identified as a gap in the curriculum. A capstone design or group design project that is multidisciplinary and covers multiple areas of specialty is the predominant approach followed by most of the universities.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".