MétaCan
Menu
Back to cohort
Record W2024610583 · doi:10.1017/s0890060415000013

Analogical thinking: An introduction in the context of design

2015· article· en· W2024610583 on OpenAlexaff
Ashok K. Goel, L. H. Shu

Bibliographic record

VenueArtificial intelligence for engineering design analysis and manufacturing · 2015
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsContent (measure theory)Context (archaeology)Computer scienceAction (physics)World Wide WebMultimediaMathematicsHistory

Abstract

fetched live from OpenAlex

Analogical thinking is a core process of design thinking.This is because design is a cognitive activity (e.g., Cross, 2004;Visser, 2006), and analogy is a core process of cognition (e.g., Hofstadter, 2001;Thagard, 2005).Of course, design is a very wide-ranging and open-ended cognitive activity.For example, design typically is situated in and distributed over the physical world (e.g., design materials), the information world (e.g., design libraries) and the social-cultural worlds (e.g., design teams).Yet, theories, techniques, and tools of analogical design (sometimes also called design by analogy) so far have been much more limited.If we look at the current theories of analogical design, they do not fully capture the range and variety, or the open-endedness and richness of design.Goel (1997) presents an early analysis of cross-domain analogical design, and Goel and Craw (2005) provide a more recent review of within-domain case-based design.Thus, this Special Issue of AI EDAM on analogical thinking has three goals.First, it seeks to explore and use current theories of analogy to understand design as a cognitive activity.Second, it seeks to identify new problems in design for spurring the development of new theories and techniques of analogy.Third, it summarizes the current state of the art in analogical thinking in design and engineering at the end of 2014.The Special Issue contains seven highly refereed papers that represent a subset of all initial submissions.Each paper went through two rounds of reviewing and revision.After the first round, we culled all submissions down to nine papers and invited their authors to revise their papers.After the second round of reviewing, we further pruned the papers to just seven; we recommended the other two good papers for a regular issue of AI EDAM because they were not quite ready for this Special Issue.We also requested authors of the seven extant papers to significantly shorten their articles to fit into the Special Issue.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0020.013
Scholarly communication0.0060.008
Open science0.0020.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0100.002

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 head0.093
GPT teacher head0.296
Teacher spread0.203 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueArtificial intelligence for engineering design analysis and manufacturingSame topicDesign Education and PracticeFrench-language works237,207