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Record W2006798189 · doi:10.1155/2012/148287

Revisiting the Role of Neighbourhood Change in Social Exclusion and Inclusion of Older People

2011· article· en· W2006798189 on OpenAlexafffundabout
Victoria F. Burns, Jean‐Pierre Lavoie, Damaris Rose

Bibliographic record

VenueJournal of Aging Research · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à MontréalMcGill UniversityCentre de Santé et de Services Sociaux Cavendish
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNeighbourhood (mathematics)Inclusion (mineral)MedicineSocial exclusionInclusion–exclusion principleGerontologySociologyGender studiesEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Objective. To explore how older people who are "aging in place" are affected when the urban neighbourhoods in which they are aging are themselves undergoing socioeconomic and demographic change. Methods. A qualitative case study was conducted in two contrasting neighbourhoods in Montréal (Québec, Canada), the analysis drawing on concepts of social exclusion and attachment. Results. Participants express variable levels of attachment to neighbourhood. Gentrification triggered processes of social exclusion among older adults: loss of social spaces dedicated to older people led to social disconnectedness, invisibility, and loss of political influence on neighbourhood planning. Conversely, certain changes in a disadvantaged neighbourhood fostered their social inclusion. Conclusion. This study thus highlights the importance of examining the impacts of neighbourhood change when exploring the dynamics of aging in place and when considering interventions to maintain quality of life of those concerned.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.210
Threshold uncertainty score0.418

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.011
Scholarly communication0.0040.004
Open science0.0020.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.080
GPT teacher head0.391
Teacher spread0.310 · 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 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

Citations178
Published2011
Admission routes3
Has abstractyes

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