Bibliographic record
Abstract
This chapter discusses three overlapping and multi-disciplinary themes in the architectural design process, including real-time feedback, human behavior as a computational data source, and reconsiderations of comfort and experience to consider gradients of performance. The chapter highlights projects that are experimental structures pointing to promising new trajectories for 'computing the environment' that offer perspectives not seen in mainstream practice. These projects are part of a larger movement in architecture, both in school and in practice, to design and build 1:1 prototypes and pavilions that serve as influential test beds for new ideas. The impact of these temporary pavilions and pop-ups has been studied in recent architectural essays, for the relevance they hold in relation to larger architectural ideas, in the context of digital design and virtual spaces, and these reveal a plurality of approaches. Architect Philippe Rahm is known for his experimental architectural proposals that push the boundaries of environmental design. The increasing ease and speed of gaining feedback from physical and virtual testing enables new ways of designing. Real-time feedback, design for interaction with the environment, not only measuring or simulating its behavior, and the inclusion of new metrics like sound, are flourishing in experimental projects, and are likely to come to mainstream practice in the near future.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".