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Record W4413318852 · doi:10.1109/mahc.2025.3598651

Solids, Parameters, and Programs: Computation for Early-Stage Architectural Design

2025· article· en· W4413318852 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

VenueIEEE Annals of the History of Computing · 2025
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputationStage (stratigraphy)Computer scienceSoftware engineeringEngineering drawingEngineeringProgramming languageGeology

Abstract

fetched live from OpenAlex

In 2025, computation pervades architecture. No one idea or technology dominates. Architectural practice comprises many processes, so we should not be surprised at the wide diversity of computational tools used. Here though, I focus on how computation has supported the early part of design—that often brief period that sets the overall organization of a project. Computer-aided architectural design (CAAD) researchers have long aimed to support such “early-stage architectural design.” Typically, the term remains an aspirational goal, rather than a sharply defined objective for research. And it does not translate directly to practice, which has opportunistically adapted computational tools developed, at least initially, for other purposes. This article examines three related computational devices that have played important, though not complete, roles in early-stage architectural design. First, solid modeling systems enable computational sketches of early ideas. Second, parametric modeling requires design structure, but defers many decisions to later design stages. Third, end-user programming tools encourage prototyping to support very early-stage decision-making.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.661
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.067
GPT teacher head0.276
Teacher spread0.209 · 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