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Record W3096389472 · doi:10.1080/10511482.2020.1806899

Advocating for the Preservation of Senior Housing: A Coalition at Work Amid Gentrification in Detroit, Michigan

2020· article· en· W3096389472 on OpenAlexaff
Tam Perry, Lisa Berglund, Julie Mah, Claudia Sanford, Pamela Schaeffer, Evan W. Villeneuve

Bibliographic record

VenueHousing Policy Debate · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsUniversity of TorontoDalhousie University
Fundersnot available
KeywordsGentrificationScholarshipWork (physics)Affordable housingPolitical sciencePublic relationsSociologyPublic administrationEconomic growthLawEconomicsEngineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.091
GPT teacher head0.339
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2020
Admission routes1
Has abstractyes

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