MétaCan
Menu
Back to cohort
Record W4400518008 · doi:10.1080/07352166.2024.2372027

Networked everyday lives in the ‘care-full’ city: A framework for examining the complexities of immigrants living with dementia, carepartners, and care workers

2024· article· en· W4400518008 on OpenAlexafffundabout
Samantha Biglieri, Justine Bochenek, Salma Abdalla, Maxwell Hartt, Kimberly J. Lopez, Roger Keil, Rachel Weldrick

Bibliographic record

VenueJournal of Urban Affairs · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsYork UniversityUniversity of WaterlooQueen's UniversityToronto Metropolitan University
FundersSocial Sciences and Humanities Research Council
KeywordsImmigrationDementiaSociologyGerontologyResidential careAging in placeCare workAssisted livingPsychologyMedicinePolitical scienceWork (physics)

Abstract

fetched live from OpenAlex

The ways in which people living with dementia (PLWD) care and are cared for in urban and suburban environments is not fully understood. Specifically, there is limited research on the impact neighborhoods have on the intersectional experiences of well-being for PLWD and their formal and informal caregivers—particularly for immigrants, women, and those living in under-resourced suburban areas. Writing from the geographical context of the inner suburb of Scarborough in Toronto, Canada, we present a framework for understanding the everyday complexities of care practices in this triad of individuals—PLWDs, care partners, and care workers—that considers their diverse practices, contexts, identities, and relations through time. Documenting these networks of caring complexities, represented in social and spatial ways, can disrupt the ways in which neoliberalism has relegated the understanding of care to be a highly gendered and racialized problem, as well as an individual problem to be solved by the free market, and to be contained in the home between family members (not in the public community realm).

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.029
GPT teacher head0.289
Teacher spread0.260 · 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 designQualitative
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

Citations1
Published2024
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Urban AffairsSame topicMigration, Aging, and Tourism StudiesFrench-language works237,207