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Record W4386268152 · doi:10.1080/26892618.2023.2245814

Healthy Housing in Richardsville: A Photovoice Project on Community Livability for Aging Adults

2023· article· en· W4386268152 on OpenAlexaboutno aff
Terri Lewinson, Gaynell M. Simpson, Yisiara Aileen Aguirre Rodriguez, Justice Nagovich, Sophia Allen

Bibliographic record

VenueJournal of Aging and Environment · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsnot available
Fundersnot available
KeywordsAffordable housingAging in placeRentingAtlantaSubsidized housingPhotovoiceApartmentWalkabilityGerontologyGentrificationUnit (ring theory)Liberian dollarQuarter (Canadian coin)SociologyPsychologyPublic housingEconomic growthBusinessMetropolitan areaBuilt environmentGeographyPolitical scienceMedicineEconomicsFinanceEngineering

Abstract

fetched live from OpenAlex

Aging in place refers to staying in one’s home well into older age. When gentrification occurs, older adults face financial challenges in sustaining affordable housing. Resident displacement results when moderate to high-income home buyers stream into neighborhoods and drive property values to prohibitive levels. Richardsville (pseudonym) is one of many communities in Atlanta’s inner city that has transformed from a blighted, long-derelict area to a hotbed of high rent apartment units, mixed with quarter-million-dollar-plus homes. Richardsville Senior Residences, an affordable rental unit, was built to provide housing for a mix of incomes and ages and retain longtime residents in Atlanta’s neighborhoods. In this community-based research project, we explore the lived experiences of eight older adults in a series of focus groups to discuss photographic images and give meaning to understand how they described aging in place. Common narratives among participants include access to outdoor spaces, smoke-free facilities, health and wellness, access to services, and social connections.

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)0.013
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 score0.400
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.516
GPT teacher head0.590
Teacher spread0.074 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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