A Typology of Very Small Dwellings: Lessons from 15 Years of Permanent Supportive Housing
Bibliographic record
Abstract
Recent building and zoning code changes in the United States and Canada have significantly reduced the minimum size of a dwelling: from 290 to 220 square feet in San Francisco and from 400 to 300 square feet in New York City. Market-rate developers can plan for these new opportunities by turning to examples of well-designed small dwellings from non-profit developers, who have been building such apartments in permanent supportive housing projects for people transitioning from chronic homelessness. This paper presents a typological study of very small studio apartments from North American permanent supportive housing (PSH), formulating a set of spatial descriptors within a typological framework. This paper is grounded in the scholarship on permanent supportive housing and the particular needs of the residents, as well as the emerging literature on very small dwellings. The classifications understood by this study include: width and depth and width/depth ratio; entry sequence; kitchen type and kitchen location; storage size and allocation; bathroom fixture types and layout. Space syntax diagrams reveal that the overall layout is determined primarily by the entry sequence, has two primary diagrams, dependent on whether the resident walks directly into the kitchen or into a distinct entry hall. The placement of each additional component hinges on this first decision. PSH units are a resource for all human-centered designers, whether developing market rate or subsidized housing units. The patterns noted in this study establish design guidelines which will allow teams to learn from existing unit designs. This research has increased benefits for the designers and developers of housing for formerly homeless individuals, as many new communities throughout the country are recognizing the benefit of PSH when addressing the pervasive spread of homelessness.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".