Augmenting Human Designers and Builders: Augmentation Discussed in Architectural Design Research
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
Rapid developments in Artificial Intelligence and Augmented Reality (AR) are opening the possibilities for a closer relationship between humans, computers and machines, requiring multiple fields and industries to rethink the role of humans in the production chain. This chapter presents several research projects as case studies to discuss the issue and pose further questions on how architects should respond. With the commercialisation of various devices, Virtual Reality and AR are becoming increasingly popular topics in numerous industries, including design and architecture. The structure is a prototype for an adaptive design and fabrication system, resilient to wide tolerances in material behaviour and fabrication accuracy while being the largest structure to date built on the principles of AR-assisted fabrication. The design is inspired by a traditional Métis sash, which is made with the art of finger weaving, and draped across one's shoulder or tied around one's waist. The aim is to develop an adaptable workflow and toolset, applicable to various design-to-fabrication scenarios.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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