Contextual influences on trade-offs in engineering design: a qualitative study
Bibliographic record
Abstract
Abstract Design occurs in complex socio-technical contexts with conflicting stakeholder goals, requirements and other constraints. These limit solution options and create trade-offs where improvements relative to one goal come at the expense of performance on another. Little is known about how the design context influences trade-offs, or how designers interact with context to manage trade-offs. This article reports on an exploratory qualitative interview study investigating design trade-offs in relation to their socio-technical context. We identified nine themes reflecting engineering designers’ perceptions of the influence of the design context on their ability to resolve trade-offs. Findings suggest that the design context is both a source of trade-offs, and of knowledge and information that helps designers clarify ambiguous requirements to navigate and resolve trade-offs. The results provide insight into how designers interact with the design context to learn about the structure of their design problems and the degrees of freedom available to resolve trade-offs. The findings also contribute to understanding the effects of path dependencies in trade-off situations, and how the sequential distribution of design decisions over time constrains trade-off resolution. We discuss some of the goals and challenges of conducting rigorous qualitative research in design and identify potential directions for further research.
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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 | 0.084 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".