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Record W2732617637 · doi:10.1093/geroni/igx004.3433

FROM ‘ACTIVE’ TO ‘PRECARIOUS’ AGEING: GLOBALIZATION AND THE RECONSTRUCTION OF THE LIFE COURSE

2017· article· en· W2732617637 on OpenAlexaff
Chris Phillipson, Amanda Grenier

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFraming (construction)Active ageingLife course approachInequalityPolitical sciencePoliticsGlobalizationPolitical economySociologyOlder peopleEconomic growthDevelopment economicsEconomicsPsychologyGerontologySocial psychologyEngineering

Abstract

fetched live from OpenAlex

Ideas relating to ‘successful’ and ‘active’ ageing have become firmly embedded in research and policy over the past decade. The idea of ‘active ageing’ has been especially prominent in shaping policies towards older people, with a strong emphasis on the link between activity, labor force participation and health and well-being. However, this approach has run alongside the impact of declining social protection and rising levels of social inequality. This paper examines the tension between theories that emphasise productivity and participation on the one side, and a political economy promoting new forms of risk on the other. The paper explores the extent to which the concept of ‘precariousness’ can provide a framework to address the reality of unequal access to the ideals of ‘successful aging’ and the benefits of longevity. The paper provides an assessment of the policy implications of re-framing ageing from ‘active’ and ‘successful’ to ‘precarious’ and ‘insecure’.

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)0.004
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.023
Scholarly communication0.0060.007
Open science0.0010.006
Research integrity0.0020.002
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 head0.117
GPT teacher head0.409
Teacher spread0.292 · 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
Published2017
Admission routes1
Has abstractyes

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