From History to Architectural Imagination: A physical ambiences laboratory to interpret past sensory experiences and speculate on future spaces
Bibliographic record
Abstract
The built environment offers an impressive corpus of spatial typologies on which architects and users may build their knowledge based of ambiences. This corpus primarily consists of existing buildings originating from a wide spectre of historical backgrounds, but it could also include past ambiences. A physical ambiences laboratory has recently been developed, built, and inhabited to compare existing and past ambiences, but foremostly speculate on future ambiences. It consists of a full-scale, adaptable structure that allows for the experience of architectural typologies, and enables spatial transformations through time. Building the adaptable structure in an outdoors environment aims to connect the theory of ambiences with the actual complexity of experiencing on a site, which cannot be adequately approached with digital simulation. The definition of an ambience therefore involves complexity because of the changing nature of the environmental conditions that generates it, such as light, wind, sun, and sound, creating varying distribution patterns of natural fluxes. This research-creation project recognizes that the people-environment issue constitutes an essential basis to the creation of genuine genius loci. The research suggests that adaptive opportunities and reinterpretation of existing ambiences could ultimately translate into new spaces to experience environmental delight for responsive inhabitants.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".