Advocating for the Preservation of Senior Housing: A Coalition at Work Amid Gentrification in Detroit, Michigan
Bibliographic record
Abstract
As cities become increasingly gentrified, the experiences of their oldest and longest residents often go underrecognized in favor of class-based and racialized concerns about displacement. Underrepresented in both scholarship and organizing efforts, eviction and displacement pose unique threats to seniors because of the link between their health and housing needs. To uncover possible strategies for coalition building and senior housing policy advocacy in quickly changing neighborhoods, this article examines the strategic efforts of Senior Housing Preservation-Detroit (SHP-D). Originally formed in 2013 to address the displacement of a single building of seniors, SHP-D aims to raise awareness of and advocate to preserve housing in a city whose core is rapidly changing. In this article, we offer an overview of the coalition’s advocacy as a way to highlight the role of community mobilization toward preserving affordable senior housing. We outline (a) the formation of the coalition, (b) recent developments, (c) strategic planning processes, and (d) lessons learned by this coalition that may be useful for other senior housing advocacy efforts. We conclude by addressing SHP-D's attention to immediate health needs of older adults in congregate housing due to the COVID-19 pandemic.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.009 |
| 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".