A Proposed New Graduate Program in Technical Product Innovation at UBC
Bibliographic record
Abstract
UBC has held two NSERC Chairs in Design Engineeringthrough which we have established connections with theSauder School of Business (through our New VentureDesign program) and other engineering departments(through our multidisciplinary capstone design course).We are now planning a renewal application for ourDesign Chair that aims to extend the scope of designrelatedwork at UBC by establishing a graduate programin Technical Product Innovation (TPI) that will connectApplied Science, the Business School, Computer Scienceand Industrial Design at the Emily Carr University of Artand Design. The TPI program will offer both thesis andnon-thesis degree options at the master's level, along witha concentration certificate that will be available to non-TPI students. The curriculum will have five keycomponents: (1) a technical base in the area of thestudent's undergraduate specialization, (2) courses ininnovation theory and entrepreneurship in collaborationwith the Sauder School, (3) courses in human factorsengineering in collaboration with computer science,(4) special 'passport-style' skills-based courses inprototyping, visual communication and user evaluation incollaboration with industrial design, and (5) project andthesis options that will allow for work on industry- orentrepreneurship-based design projects. The goal of theproposed program will be to train students to take onleadership roles in product development programs in bothstartups and established companies. The TPI programwill initially focus on three key areas: medicaltechnologies, consumer products and business-to-businessproducts. It will build on two significant cross-universityinitiatives - the multi-faculty Human-ComputerInteraction interest group (hci@ubc) and the universitywideEntrepreneurship@UBC program - and will tie intorelated emerging initiatives such as the Innovation Hub atVancouver General Hospital and Wearables@UBC.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.158 | 0.056 |
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".