What do Design Data say About Your Model? - A Case Study on Reliability and Validity
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
Parametric modeling systems are widely used in architectural design. Their use for designing complex built environments raises important practical challenges when composed by multiple people with diverse interests and using mostly unverified computational modules. Through a case study, we investigate possible concerns identifiable from a real-world collaborative design setting and how such concerns can be revealed through interactive data visualizations of parametric models. We then present our approach for resolving these concerns using a design analytic workflow for examine their reliability and validity. We summarize the lessons learnt from the case study, such as the importance of an abundance of test cases, reproducible design instances, accessing and interacting with data during all phases of design, and seeking high cohesion and decoupling between design geometry and evaluation components. We suggest a systematic integration of design modeling and analytics for enhancing a reliable design decision-making.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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