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Record W4402137340 · doi:10.1080/15350770.2024.2396126

Intergenerationality in the Context of Age-Friendly Cities and Communities: Older People’s Experiences and Perspectives on Place and Community Living in the UK

2024· article· en· W4402137340 on OpenAlexaff
Judith Sixsmith, Ryan Woolrych, Hannah Loret, Meiko Makita, Mei Fang Lang

Bibliographic record

VenueJournal of Intergenerational Relationships · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsSimon Fraser University
FundersEconomic and Social Research Council
KeywordsContext (archaeology)Aging in placeGerontologyOlder peopleSociologyGeographyMedicine

Abstract

fetched live from OpenAlex

Intergenerational communities can be understood as communities where multiple age groups interact, feel valued, and contribute to community life in an inclusive way. However, older adults in many communities in the UK can be excluded from intergenerational mixing. The PlaceAge project undertook interviews, photo-diaries, community mapping workshops, and knowledge cafés to explore older adult experiences of and participation in their cities and communities. Three key themes were generated, showcasing intergenerationality: (1) Connectedness in place and space; (2) Feeling old in siloed communities; and (3) Play in everyday life. This research emphasizes the importance of inclusive and accessible intergenerational places and activities that foster sustainable social connections and combat ageism. It highlights the value of playfulness, skill-sharing and co-mentoring, and advocates for the importance of incorporating intergenerational opportunities into the planning and development of age-friendly cities and communities.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.008
Scholarly communication0.0050.004
Open science0.0010.010
Research integrity0.0010.002
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 head0.065
GPT teacher head0.320
Teacher spread0.255 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

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