QUESTIONNAIRE FOR ASSESSING DESIGN PRACTICES
Bibliographic record
Abstract
In 2010, an exploratory survey on methods, tools and design techniques (MTT) has led to a better understanding of design practices of small and medium enterprises (SMEs) located in Eastern Quebec. The results of this survey have shown that most companies report having a structured design process, while only some resort to MTT. Following this study, a new research was launched to develop performance indicators for the design process, but also, and more generally, for design projects. To do this, a tool was developed to characterize and evaluate the design practices of companies as a way to establish performance indicators following a longitudinal study. A questionnaire was developed as a tool based on a model of design system consisting of six dimensions (human, management, environment, product, process and techno-scientific). In the questionnaire, each dimension is characterized by different descriptors which are analyzed by four aspects that measure: i) if the descriptors are taken into consideration (the occurrence of the descriptors), ii) the importance of the descriptors according to the project success , iii) the company’s performance level related on the descriptors and iv) the involvement of partners. For validation purposes, the tool was tested on a pilot basis by eight companies. This paper introduces the questionnaire and the model that directed its construction and the types of analysis that can be conducted using the data collected.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".