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Record W2105974517 · doi:10.5555/2664323.2664349

Parameters tell the design story: ideation and abstraction in design optimization

2014· article· en· W2105974517 on OpenAlex

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsAutodesk (Canada)
Fundersnot available
KeywordsIdeationDocumentationComputer scienceDesign thinkingWorkflowAbstractionSchematicProduct designPoint (geometry)Probabilistic designDesign briefProduct (mathematics)Software engineeringEngineering design processEngineering drawingHuman–computer interactionSystems engineeringEngineeringDesign technologyPsychologyProgramming languageCognitive scienceMathematicsMechanical engineering

Abstract

fetched live from OpenAlex

We report qualitative findings from interviews and observations detailing how professionals generate and evaluate design ideas using design optimization tools. We interviewed 18 architects and manufacturing design professionals. We frame our findings using the Geneplore model of creative cognition and classify examples of ideation and abstract design thinking arising from optimization workflows. Contrary to our expectations, we found that the computed optimum was often used as the starting point for design exploration, not the end product. We also found that parametric models, plus their associated parameters and simulations, serve as an alternate, highly valued form of design documentation distinct from engineering schematics. 1.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.899
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.032
GPT teacher head0.231
Teacher spread0.199 · 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

Quick stats

Citations79
Published2014
Admission routes1
Has abstractyes

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