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Record W4415412740 · doi:10.1111/1468-4446.70042

Aging in Nationhood: Everyday Nationalism and Belonging Among Seniors in Old‐Age Homes in Québec

2025· article· en· W4415412740 on OpenAlexafffundabout
Jessica Stallone

Bibliographic record

VenueBritish Journal of Sociology · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Identity and Heritage
Canadian institutionsUniversity of Toronto
FundersFonds de Recherche du Québec-Société et Culture
KeywordsEthnographyNationalismMeaning (existential)Everyday lifePrecarityWhite (mutation)

Abstract

fetched live from OpenAlex

Scholars of aging and nationalism rarely engage with each another. To remedy this gap, I examine how ethnonationalism becomes a resource for navigating the precarity of aging. Drawing on ethnographic fieldwork in two private senior residences in a region of Québec, I show how financially privileged Québécois seniors enact nationhood through everyday cultural practices. I introduce the concept of "aging in nationhood" to describe how seniors draw on ethnonationalist identities to foster comfort, community, and meaning at an age of decline-often with exclusionary effects. Seniors who do not-or cannot-assimilate into majority culture experience social isolation. By linking nationalism and aging, I show how seniors reproduce the nation, shaping their well-being and the boundaries of belonging. While grounded in Québec, this concept offers new insight for thinking about how dominant-group seniors mobilize ethnonationalism as a source of membership and exclusion in white aging societies across the Atlantic.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.054
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.005
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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