Bibliographic record
Abstract
We propose a roundtable discussion on “Design for Inclusion” and how to integrate this concept into the undergraduate engineering curriculum. Designing devices and technology that benefit and are useful to all members of society and take into account diverse users' needs does not seem to be a common practice in industry nor in the classroom. We all know examples of devices that seem to have “missed the point” for large segments of their user base. We believe diverse teams are needed to brainstorm ideas from the start and to bring such products to market. The revolution in thinking needs to start at the source, where students are being educated. How can “design for inclusion” become integrated into our design education? The panelists who will discuss this theme will be: • Beth Kolko, Associate Professor in the Dept of Human Centered Design & Engineering, University of Washington, USA (confirmed) • Benoit Gervais, Design engineer, Principal, Futurescape, Ottawa (confirmed) • Elizabeth Croft, Professor of Mechanical Engineering, University of British Columbia (confirmed) • Li Shu, Associate Professor of Mechanical and Industrial Engineering, University of Toronto (confirmed) • Sarah Shortreed, Vice-President, Enterprise Portfolio Management, BlackBerry, Research in Motion (confirmed) • Antony Hodgson, NSERC Chair in Design Engineering, University of British Columbia (confirmed) The panel will be moderated by Catherine Mavriplis, University of Ottawa. We invite the community to join in the discussion with the panelists. These could include representatives of Engineers Canada, the licensing bodies, other NSERC Design Chairs, Deans and Curriculum Chairs, professors and students, industry and government.
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 | 0.135 | 0.129 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.031 | 0.040 |
| Open science | 0.015 | 0.034 |
| Research integrity | 0.085 | 0.060 |
| Insufficient payload (model declined to judge) | 0.056 | 0.015 |
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".