Three Types of Architectural Educational Strategies (AES) in Sustainable Buildings for Learning Environments in Canada
Bibliographic record
Abstract
This article explores a trend provisionally called “eco-didacticism” observable for nearly 15 years in art, design and architecture. The corpus concentrates on learning centres as buildings meant to diffuse advanced knowledge in the field of sustainable architecture. We found evidence of additional educational intentions to the pedagogical or scientific programs that these buildings have already been mandated to host and support. A variety of practices or devices have sometimes been added to the architecture, sometimes integrated, while others determine the overall structuring of these educational buildings. Seven cases of “learning centres” built in Canada between 2004 and 2018 have been screened through three epistemological filters distinguishing forms of “architectural didactics”: 1—a labeling often quantitative approach, 2—an experiential or practical approach, 3—a visually narrative or iconic approach. While outlining definitions of these Architectural Educational Strategies (AES), we offer initial explanations for their distinctive features. It appears that architects, designers and critics altogether operate on the belief that forms of architectural communication can operate as elements of a language that would be accessible to non-experts. Our conclusion indicates how future research could question the very possibility of giving lessons through formal language and aesthetic features.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".