How Many Times Is Enough? Rationalizing Program and Optimizing Performance Through [Repeated] Human-Building Interaction
Bibliographic record
Abstract
Performance in architecture today supersedes the simple characterization of “devising a set of practical solutions to a set of largely practical problems” (Kolarevic, 2004), to now place emphasis on what the building does across numerous dimensions -- notably how it affects and how it transforms -- based on quantifiable, qualifiable and intangibilities of the architectural ‘object’. On the other side, we question how buildings are interpreted, which is contingent on the idea of interactivity – the interaction between buildings and their users, or the container and the contained. In this discourse, however, too much emphasis has been placed on the purely spatial aspects of experiencing architecture at the expense of other understandings – one of which is, vitally, the concept of time. The goal of this paper, therefore, is to explore how (the frequency of) human-building interaction can constitute the basis for decisions surrounding programming and design optimization. This research, considering space + time in concert, deploys meta-analysis of literature, coupled with case studies and logical argumentation, to shape a provocation and proposition.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".