Introducing and Sustaining Traditional Fabrication Methods in the Context of Teaching Prototyping for Engineering Design
Bibliographic record
Abstract
The introduction of Makerspaces across Canada has made it much easier for universities to provide engineering students the opportunity to “close the loop” on engineering design, by giving students the means to implement their projects. However, with the introduction of newer rapid prototyping technologies such as 3D printing, students forget or are unware that traditional fabrication technologies can often be more efficient then these new technologies, depending on the situation.This paper discusses the development and sustainability of traditional fabrication methods through the development of low cost dedicated facilities used in interdisciplinary engineering design courses. The introduction of these traditional fabrication methods have proven to increase the efficiency, creativity and critical thinking of engineering students related to the development of quick and iterative prototypes for their engineering designs. However, much work remains to be done if these facilities are to be optimized and sustained in this new engineering education paradigm.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".