Solids, Parameters, and Programs: Computation for Early-Stage Architectural Design
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
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".