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Record W4200076946 · doi:10.1093/geroni/igab046.1534

At-Homeness: Rethinking Personhood-in-Community Through the Lens of Social Identity

2021· article· en· W4200076946 on OpenAlexaff
Daniel R Y Gan, Graham D. Rowles, Habib Chaudhury

Bibliographic record

VenueInnovation in Aging · 2021
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPersonhoodSociologyEthnographyIdentity (music)DementiaGender studiesGerontologyAnthropologyEpistemologyAestheticsMedicine

Abstract

fetched live from OpenAlex

Abstract Since Chaudhury’s seminal work (2008), spatial ethnographies of the everyday lives of people living with dementia(PLWD) have proliferated. From an experiential perspective, geographies of home (Blunt & Varley, 2004) and geographies of dementia may overlap significantly. We conducted a meta-ethnographic synthesis of n=28 articles to identify points of convergence and divergence in these literatures using comparative thematic analysis with NVivo 12. Whereas geographies of home highlight at-homeness (e.g., ontological safety and daily activities), geographies of dementia underscore communal and civic participation (e.g., social relationships). These themes converge around “social identity” which may be an important construct that helps PLWD feel at home. The quality of life of PLWD in the community may be influenced by prior (and present) experiences of at-homeness. These become more pertinent as older adults shelter in place. We discuss the implications of these findings in relation to relational models of personhood-in-community (Swinton, 2020) and community gerontology.

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 imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.006
Science and technology studies0.0090.056
Scholarly communication0.0140.021
Open science0.0030.014
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.108
GPT teacher head0.427
Teacher spread0.318 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2021
Admission routes1
Has abstractyes

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