Designing Affordable Housing for Adaptability: Principles, Practices, & Application
Bibliographic record
Abstract
While environmental and economic sustainability have been driving factors in the movement towards a more resilient built environment, social sustainability is a factor that has received significantly less attention over the years. Federal support for low-income housing has fallen drastically, and the deficit of available, adequate, affordable homes continues to grow. In this thesis, I explore one way that architects can design affordable housing that is intrinsically sustainable. In the past, subsidized low-income housing has been built as if to provide a short-term solution—as if poverty and lack of affordable housing is a short-term problem. However, I argue that adaptable architecture is essential for the design of affordable housing that is environmentally, economically, and socially sustainable. Further, architects must balance affordability, durability, and adaptability to design sustainable solutions that are resistant to obsolescence. I conclude by applying principles and processes of adaptability in the design of Apto Ontario, an adaptable affordable housing development in the low-income historic downtown of Ontario, California (Greater Los Angeles). Along a new Bus Rapid Transit corridor, Apto Ontario would create a diverse, resilient, socially sustainable community in an area threatened by the rise of housing costs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".