Bibliographic record
Abstract
If the teaching of engineering is indeed the practice of engineering, then it stands to reason that the development of engineering curricula can be treated as an engineering design problem. In this paper, the authors apply the engineering design process to develop a list of courses, for a Mechanical Engineering Program, that conforms to the constraints of the Canadian system of engineering accreditation. For the purpose of this exercise, the following steps are used to define the engineering design process: identical and delimit the problem, establish the outline of the solution (and alternatives), break the problem into its constituent parts, analyze the parts, synthesize the parts into a final configuration, and document the solution. The limits and constraints on the solution are based on the criteria specified by the Canadian Engineering Accreditation Board (CEAB), the syllabus specified by the Canadian Engineering Qualifications Board (CEQB), some common rules-of-thumb, and previously published work by the authors. By utilizing the engineering design process, schools of engineering and applied science can ensure that their curricula, at least at the level of the course specification, will conform to the CEAB and CEQB requirements. As a final exercise, variations on the curriculum are studied to analyze the possibility of introducing such additional elements as options and minors, expanded studies in the arts and humanities, and development of skills in additional languages.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".