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Record W2757061000 · doi:10.15453/0191-5096.3875

When ‘Places’ Include Pets: Broadening the Scope of Relational Approaches to Promoting Aging-in-Place

2017· article· en· W2757061000 on OpenAlexafffund
Ann M. Toohey, Jennifer Hewson, Cindy L. Adams, Melanie Rock

Bibliographic record

VenueThe Journal of Sociology & Social Welfare · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchAlberta Innovates
KeywordsSituatedAutonomyAging in placeScope (computer science)FeelingSolidarityWelfareAnimal welfarePublic relationsSociologyPsychologyPolitical scienceSocial psychologyGerontologyMedicinePoliticsEcology

Abstract

fetched live from OpenAlex

Aging-in-place is a well-established concept, but discussions rarely consider that many older adults live with pets. In a ‘pet-friendly’ city, we conducted semi-structured interviews to explore perspectives of community-based social support agencies that promote aging-in-place, and those of animal welfare agencies. Applying a relational ecology theoretical framework, we found that pets may contribute to feeling socially- situated, yet may also exacerbate constraints on autonomy experienced by some older adults. Pet-related considerations at times led to discretionary acts of more-than-human solidarity, but also created paradoxical situations for service-providers, impacting their efforts to assist older adults. A shortage of pet-friendly affordable housing emerged as an overarching challenge. Coordination among social support and animal welfare agencies, alongside pet-supportive housing policies, will strengthen efforts to promote aging-in-place in ways that are equitable and inclusive.

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 score0.671
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.084
GPT teacher head0.355
Teacher spread0.271 · 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

Citations20
Published2017
Admission routes2
Has abstractyes

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