Bibliographic record
Abstract
In the Fall 2010 semester, Mount Royal University’s Engineering Transfer Program ran its first version of a new Engineering Design course that combined two term courses into one. The course consisted of integrated Technical Writing, Technical Drawing and Sketching, and Design elements. The Design element, consisting of three hours/week of in-class activities, focused on the introduction of elements of the design process, activities to expose students to the real-life aspects of each stage of the process, and a term project that brought all elements of the course together in one real-life application. The term project was the (re)design of a pedestrian footbridge on the Mount Royal (MRU) campus. The existing bridge, approximately 3.5 m long by 2 m wide, spans a man-made gulley and joins two parts of a pedestrian walkway. Relevant MRU grounds staff were involved in the project as clients and more than 100 students worked in small groups (3-5 members each) to design and then build a 1/3rd scale model of their bridge design using a somewhat constrained materials list and basic wood shop facilities. Components of the project were also integrated with other courses in the curriculum, such as Statics. This paper details the logistics of the course and of the project, as well as the lessons learned in terms of things that worked well and those that did not. Overall, the project was deemed a solid success based on feedback from students, clients and instructors. This kind of project could also be carried out at other Canadian university campuses with minimal alterations.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
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".