Bridging Indigenous practices and neuroscience for inclusive architectural design
Bibliographic record
Abstract
The built environment significantly shapes cognition, sensory processing, well-being, and the way that humans interact with one another. This research explores the intersection of the built environment, traditional and Indigenous architectural practices, and neuroscience as they apply to cognitive health and neurodivergent-friendly design. Drawing on four cultural traditions—Longhouses and other structures from Canada, Vastu Shastra from India, Feng Shui from China and the Te Aranga principles of New Zealand—the discussion synthesizes culturally sourced and peer-reviewed literature to examine how traditional design principles align with contemporary scientific perspectives. The review is structured into three sections: (1) an analysis of traditional architectural practices, (2) an evaluation of neuroscience findings on factors including natural light, spatial openness, and community spaces, and (3) an exploration of how built environments can better support neurodivergent individuals. Neuroethics provides the conceptual framework for this study, emphasizing the importance of pluralistic inquiry and the responsibility of designing pragmatic spaces that respect cognitive diversity and supportive built environments.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".