joys and challenges of creating a non-accredited multidisciplinary design program in a traditional engineering faculty
Bibliographic record
Abstract
In January 2021, a new academic unit (School of Engineering Design and Teaching Innovation) was created in the Faculty of Engineering for the first time in over 20 years. After identifying needs in the job market for programs with a focus on multidisciplinary skills, the academic unit’s first task was to develop a three-year nonaccredited program, titled “Bachelor’s of Multidisciplinary Design (Internship)”. Fundamentally, the program will include core technical expertise expected from an engineering faculty but will also offer flexibility for students to explore other interests. This type of flexibility and openness may seem daunting for first year students. Therefore, example learning paths were created based on current job market trends, with more learning paths in development. This paper focuses not only on the development of such a unique program in Canada, considering insight provided through stakeholder and focus group meetings, but also the challenges associated with submitting this program proposal through all levels of university bureaucracy, from the faculty committees through to provincial review. While there have been questions, and sometimes doubts, that such a program could be fully developed, the program is on track to pass all levels of approval and welcome its first cohort of students for the Fall 2023 term.
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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 | 0.044 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.012 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".