Bibliographic record
Abstract
‘Design’ can be a noun, or a verb. Six paths for research into engineering design (as verb) are identified, they must be co-ordinated for internal consistency and plausibility. Design Research tries to clarify design processes and their underlying theories – designing in general, and particular forms, e.g. design engineering. Theories are a basis for deriving theory- based design methods. Design engineering and artistic forms of designing, industrial design, have much in common, but also differences. For an attractive and user-friendly product, its form (observable shape) is important – a task for industrial designers, architects, etc. ‘Conceptualizing’ consists of preliminary sketches, a direct entry to hardware – industrial designers work ‘outside inwards’. For a product that should work and fulfill a purpose, perform a transformation process, its functioning and operation are important – a task for engineering designers. Anticipating and analyzing a capability for operation is a role of the engineering sciences. The outcome of design engineering is a set of manufacturing instructions, and analytical verification of anticipated performance. Design engineering is more constrained than industrial design, but in contrast has available a theory of technical systems and its associated engineering design science, with several abstract models and representations of structures. Engineering designers tend to be primary for technical systems, and their operational and manufacturing processes – they work ‘inside outwards’. Hubka’s theory, and consequently design metho- dology, includes consideration of tasks of a technical system, typical life cycle, duty cycle, classes of properties (and requirements), mode of action, development in time, and other items of interest for engineering design processes. Hubka’s methodology is demonstrated by several case examples.
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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".