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Record W2162417893 · doi:10.24908/pceea.v0i0.4641

ENGINEERING DESIGN VS. ARTISTIC DESIGN – A DISCUSSION

2012· article· en· W2162417893 on OpenAlex
W. Ernst Eder

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2012
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsEngineering design processDesign briefDesign languageComputer scienceIndustrial designTask (project management)Design educationSystems engineeringProbabilistic designConsistency (knowledge bases)DesigntheoryProduct designProduct (mathematics)Design technologyEngineeringHuman–computer interactionArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

‘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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.762
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.206
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it