Rethinking concept design tools: High-level requirements for concept design tools
Bibliographic record
Abstract
In the architecture, engineering, and construction industry there is increasing recognition that design decisions early in the design process create significant project value with relatively small effort. It seems reasonable to investigate what decision support for designers in early phases should look like and what conclusions can be drawn for digital tools that designers employ in those early project phases. This paper introduces and discusses a cohesive set of concept design tool requirements. It explores connections between theoretical approaches in design cognition, experimental implementations, and recent developments in architectural practice responding to very pragmatic problems. The paper communicates results of academic workshops at the Third and Fourth International Conference on Design Computing and Cognition, DCCÂ08 and DCCÂ10, respectively, in the context of this ongoing research. At the end, it proposes a systematised model of a desired software tool thus allowing future research to close critical gaps which have hampered progress in concept design tool development.
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 | 0.103 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.018 | 0.033 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".