Realizing Innovative Senior Housing Practices in the U.S.
Bibliographic record
Abstract
Innovative methods for housing seniors have long existed. Two approaches discussed often are cohousing and accessory dwelling units (ADUs). Cohousing and ADUs are more prevalent in Denmark and Canada, respectively, than in the U.S. U.S. housing planners, policymakers, and advocates have long expressed interest in adapting and scaling up these approaches. However, few cohousing communities or ADUs have been built for seniors in the U.S. This chapter draws on semi-structured interviews with 22 U.S., Danish, and Canadian housing practitioners and experts on seniors to explore the challenges of meeting seniors’ housing needs through cohousing and ADUs in the U.S. and to understand how the challenges can be overcome. Findings show the numerous potential benefits innovative senior housing can have on seniors’ physical, social, and financial accessibility. However, barriers such as regulations and financing provide challenges and opportunities for innovative senior housing. This chapter concludes with a set of lessons learned and next steps for meeting seniors’ needs through cohousing and ADUs in the U.S. Furthermore, this chapter moves the conversation from visioning to implementation, showing how housing advocates can play a vital role in aiding seniors’ housing needs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".