D-ART for collaboration in evaluating design alternatives
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.
Bibliographic record
Abstract
Evaluating design ideas is an integral part of designing built environments. It involves multiple stakeholders with diverse backgrounds reviewing design solutions by studying their form and performance data. Although there are computational systems for supporting evaluation tasks, they are either highly specialised for designers or configured for a particular workflow with limited functions. We developed a Design Analytics method aiming at a collaborative and data-driven evaluation of alternatives in the design-evaluate-feedback cycle. Adopting this approach, we introduce D-ART as a prototype system composed of customisable Web interfaces for presenting design alternatives, enabling stakeholders to participate in data-informed discourse on alternatives and providing feedback to the design team. Its system design considers requirements gathered through literature review, critical analysis of the existing systems and collaboration with our industry partners. Finally, we assessed D-ART’s design through an expert review evaluation, which generally reported positive results on the system’s goals.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it