Bibliographic record
Abstract
Computer aided architectural design (CAAD) has changed practice, education, research and architecture itself over the last several decades.The fact of change is hardly surprising -that is what new technologies do.The kind of change is another matter.Most early CAD pundits might well have taken Neils Bohr to heart: "Prediction is very difficult, especially if it's about the future."Few imagined that computation would become such an integral part of architectural design and discourse.Terms such as digital design and digital architecture that we take as commonplace go beyond the usual measure of effectiveness and efficiency to connote deep cultural change within the discipline.Conferences like Computer aided architectural design futures (CAAD Futures), among others, are places where knowledge advances and prospects can be discussed and explored.The aim of CAAD Futures 09 was precisely to bring together professionals, researchers, educators in the domain of design in general (architects, urban planners, industrial designers, etc.), from around the world, and offering multilingual contributions (in English and French).This is why the conference was entitled "Joining languages, cultures and visions"."Joining" labeled our hope for a productive fusion of knowledge of different kinds and diverse ways of thinking from various cultures.We especially aimed to use this exceptional moment to frame common, but ambiguous concerns in design like "sustainability", "ecology", "collaboration" and "performance".In the first part this issue of the International Journal of Architectural Computing, for the first part we have asked selected CAAD Futures 09 authors to further refine their ideas; this is the special issue section.We examined the submitted papers and found that one particular keywordparametric-stood out from all others.It was everywhere from education to practice, as well as in technological developments.Moreover, it embraced a great variety of issues ranging from heritage conservation to architectural design and ideation to building performance.We can question the fact that parametric approaches are new to design or not, and the answer is a resounding "no".Indeed, the first CAD system, SketchPad, established parametric representation at the core of CAD.Some of the earliest experts, notably the late William Mitchell, patiently explained parametric concepts and capabilities over the years.Strangely, in architecture, the parametric was largely dismissed as belonging to "ordinary design".Other disciplines knew better.Parametric representations, interfaces and techniques developed apace, especially in the engineering disciplines.Architecture only recently rediscovered its past.Perhaps architecture is a slow learner.It certainly took time to adopt the concept of parametric design and exploration in education and in practice.Through multiple exchanges among the various spheres of design and issue 04, volume 08 iii
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 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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.061 | 0.040 |
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".