Development of a Tool for Assessing the Design Process: A step toward the creation of a Design Observatory
Bibliographic record
Abstract
An exploratory survey, conducted in 2010, has led to a better understanding of the design practices of Québec businesses located in the Bas-Saint-Laurent, Gaspésie, Îles-de-la-Madeleine, and Côte-Nord regions. This survey was conducted among 31 businesses from different sectors of activity focused on design methods, tools and techniques. The research aims to develop a method and a tool to assess and characterize an organization, its design system and its design process. The results will serve to establish an observatory of the design practices. Such an observatory will contribute to improve the engineering design education.The study shows, among other things, that most businesses claim to use a structured design process, although a very few resort to structured methods, sophisticated tools, or specialized design techniques. The results seem to indicate that the design process of SME located in the Lower Saint-Lawrence Area is not as organized as it could be expected.To understand the determinants of design performance better, and to develop indicators for assessing the design process, a new research has been launched.A conceptual model, derived from a review of literature, was selected to develop the indicators. Three axes (environmental, human and techno-scientific knowledge) and three aspects (organisation, processes and features of the product) are identified and used to define the design system.
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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 | 0.103 | 0.168 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".