Bibliographic record
Abstract
What is Design? The lack of consensus on a common definition for design and whether its body of knowledge constitutes a science or a discipline continue driving investigations of the nature of design. Despite the ambiguity, design is recognized as a process of creative problem solving and as such has become an integral part of modern business practices. The shift from product-focus towards user and experience focus has paved the way for interdisciplinary design research and the adoption of new investigative tools. Researchers agree that the understanding of the complexity of modern society requires holistic thinking, and therefore demands the implication of expert disciplines in the process of building design knowledge. How has the evolved mindset impacted design education? Although, interdisciplinary practices and transdisciplinary thinking have been acknowledged as a fundamental notion of building design knowledge, design programs seem to have made only timid adjustments in their curricula to address this new dimension. Traditional teaching methods and settings reveal themselves as insufficient and address little systemic thinking. Hence, alternative design approaches are being experimented with. The intent of this paper is to describe how the School of Industrial Design at the University of Montreal has reassessed its program to reflect the evolved mindset and what is being done to facilitate interdisciplinary design approaches.
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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".