Autism and Architecture: The Importance of a Gradual Spatial Transition
Bibliographic record
Abstract
The research in the following paper is developed in collaboration with the no-profit organization “Università per i Disturbi dello Spettro Autistico” (UDSA), active on the issue of the role of surrounding environment in the educational process of neuro-atypical young adults. Even though, wide range of population is diagnosed with Autism Spectrum Disorder (ASD), the literature primarily refers to childhood period of neuro-atypical individuals. The study explores how Architecture could help young adults with ASD to become more independent and discover their capabilities reducing environmental obstacles. The Autism Spectrum presents a wide range of cases and hues that does not permit the use of general guidelines for the design process, on the contrary, it requires taking into consideration the variety of attitude toward the surrounding environment. Therefore, the paper interrogates the methodological framework of Architecture to tackle the complexity of the design challenge with a trans-disciplinary approach; a variety of figures, outside architecture discipline, were involved in the research. An adaptive method has been used, based more on Greek idea of metis, the ability to take advantage of circumstances rather than using the Platonic notion of “eidos”, which referred to a determined pattern, to face the multifaceted aspects of the phenomenon. The study resulted in an Architectural project for The University of Autism Spectrum Disorder, in which the strategy of Gradient defines the spaces based on their intensity, activity and frequency. By considering weaknesses and insufficiency that has emerged during the research period, this paper proposes a lucid theory of the design process integrated with contradictory aspects of the spectrum.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.003 | 0.005 |
| 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".