Knowledge structures in engineering design: integrating multiple ‘ways of knowing’
Bibliographic record
Abstract
In this paper I discuss the different‘knowledges’ needed to design, and their implications onthe nature of reasoning in design. Donald Schön andHerbert Simon offer useful models of design thatprivilege different knowledge types, both needed to design.Schön describes a form of case-based reasoning built onmaterial examples. Simon’s focus is on abstract reasoning,defined within academic disciplines by formal rationalrelations between theoretical concepts. Andrew Abbott’smodalities of diagnosis, treatment, and inference helpto show what underpins the different ways of knowing, buthis modalities fail to account explicitly for translationbetween the various knowledge structures.A focus on knowledge shows challenges studentsmay face transitioning from sciences to design. It suggestsways that design project briefs constrain the way in whichstudents work with, and between, different knowledgestructures. It also potentially offers ways to think aboutintroducing design reasoning into engineering sciencecourse
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".