Bibliographic record
Abstract
Recent studies document the importance of well-designed facilities on the academic performance of students in language and mathematics, but there is very little research on how space dictates what is learned and how it is learned. What about learning that is not directly measurable by standardized test scores? How does architectural space affect what is learned in the “non-core” disciplines such as music, drama, dance, and the visual arts? How does the built environment affect the ways that teachers and students operate in what might be viewed as a learning collective? These are some of the central questions addressed in the present paper. These issues are first explored through a brief discussion of the main themes in school architecture research and discourse, followed by a description of how Froebel kindergartens, Reggio Emilia schools, and Waldorf schools have given attention to some of the physical elements that affect learning. Next, I explore engaging forms of adult learning and the perspectives of John Dewey. Then follows a discussion of the ways that classrooms and schools can be seen as collectives, using complexity science theory as a theoretical framework. Finally, the complexity science model is extended by including the actual physical spaces as important ‘agents’ in influencing a non-linear and dynamic system, and by drawing implications for school design based on the principles of complexity.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".