DESIGN AND DEVELOPMENT OF AN INTERDISCIPLINARY GRADUATE PROGRAM IN ENGINEERING PRACTICE
Bibliographic record
Abstract
In Innovation Studio, students learn how tomake meaningful, creative contributions to theircommunities as emerging practicing professionals workingon complex, multi-stakeholder problems involvingelements of technology, design, business, and publicpolicy.Innovation Studio builds on McMaster University’slongstanding commitment to relevance through communityengagement. In the Walter G. Booth School of EngineeringPractice (W Booth School) in the Faculty of Engineering,we encourage our students to see not just the technical sideof the international problems such as energyindependence, food security and clean water, but as anopportunity to co-create change with our global and localcommunities for the good of all human beings, society, andnature.Students immerse themselves in the communities inwhich challenges have been identified. Innovation Studiois the place and time where students bring thoseexperiences back to the School, share their learnings andexplore new ideas in a safe and familiar environment.W Booth students develop a deeper understanding of theneed for empathy as they move toward a new direction oridea. Working within the context of problem identification,the teams learn how to define a project and plan anapproach to produce meaningful work, prototypes, policyanalysis and new enterprises.This paper reports on the design and development ofInnovation Studio as well as feedback collected fromstudents through focus groups.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".