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Record W4200353730 · doi:10.1093/geroni/igab046.3633

Precarious Aging: A Working Definition

2021· article· en· W4200353730 on OpenAlexaffabout
Amanda Grenier, Chris Phillipson, Grace Martin, Abiraa Karalasingam, Karen Kobayashi, Patrik Marier, Debbie Laliberté Rudman

Bibliographic record

VenueInnovation in Aging · 2021
Typearticle
Languageen
FieldPsychology
TopicFamily Caregiving in Mental Illness
Canadian institutionsWestern UniversityConcordia UniversityUniversity of VictoriaBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsPrecaritySociologyDisadvantageInequalityLife course approachConceptual frameworkSocial inequalityGender studiesSocial sciencePolitical sciencePsychologySocial psychology

Abstract

fetched live from OpenAlex

Abstract Until recently, studies of precarity have overlooked aging and late life. This poster presents a snapshot of conceptual work in progress on a Canadian Social Sciences and Humanities Research Council (SSHRC) Insight Grant on precarity and aging. The poster outlines existing definitions and theoretical perspectives, key results, a current evolving conceptual model, and a working definition of Precarious Aging. It situates existing knowledge and definitions of precarity, highlights crucial intersectional locations of gender, im/migration and (dis)ability, and clarifies the concept of precarity in later life. Results at this point in the study are based on conceptual reviews, reviews of literature on precarity and aging, and the consideration of allied concepts. In conclusion, the concept of precarity offers a promising lens to guide research in the field of social and critical gerontology, providing a foundation for an enhanced understanding of the lives and realities of older people with regards to aging, disadvantage, and inequality.

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)0.016
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.004
Science and technology studies0.0040.041
Scholarly communication0.0080.011
Open science0.0030.010
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.068
GPT teacher head0.332
Teacher spread0.263 · 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 designTheoretical or conceptual
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
Published2021
Admission routes2
Has abstractyes

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