Biophilic school architecture in cold climates
Bibliographic record
Abstract
Designing school settings that provide a satisfying experience of nature and enhance well-being could be advantageous for children and teachers, though in cold climates prolonged periods of precipitation, restricted sunshine and low temperatures represent non-ideal conditions for fostering a connection with nature. This paper reviews research into the relationships between principles of biophilic design and well-being, with specific consideration for learning environments in cold climates. Children spend more time in school than any other place, except the home, and most of their learning activities occur indoors. Given the large portion of the day children and teachers spend within the built environment, an architect's perspective investigates these relationships. The paper examines the concepts and research findings that appear to offer the greatest potential for future architectural applications in children's learning environments. It also identifies gaps in biophilic design strategies in relation to schools and the importance of considering climatic conditions to create satisfying experiences of nature within the built environment. If biophilic design research is to lead to healthier, more comfortable school settings that present a greater connection between learning spaces and the natural environment, then to identify and define beneficial guidelines that translate readily into architecture is essential.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| 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".