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Record W2950861893 · doi:10.1017/s0144686x19000631

The conspicuous absence of the social, emotional and political aspects of frailty: the example of the<i>White Book on Frailty</i>

2019· article· en· W2950861893 on OpenAlexafffund
Amanda Grenier

Bibliographic record

VenueAgeing and Society · 2019
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsVulnerability (computing)PoliticsWhite (mutation)Power (physics)White paperGerontologySociologySocial vulnerabilityField (mathematics)GeriatricsGender studiesPsychologySocial scienceSocial psychologyPolitical scienceMedicinePsychiatryLaw

Abstract

fetched live from OpenAlex

Abstract Over the last 15 years, frailty has become a dominant discourse on late life. Taken-for-granted knowledge and practice can be seen in initiatives such as the International Association of Gerontology and Geriatrics’ White Book on Frailty . This paper begins with an overview of key themes on frailty from the biomedical literature, followed by critical literature in the social sciences and humanities. It discusses the tensions within the biomedical field, frailty as a social construction and ‘social imaginary’, practices of frailty as historically linked to political systems of care, and frailty as an emotional and relational experience. It then draws on a critical discourse analysis to assess the 2016 White Book on Frailty . Drawing on the idea of ‘significant absences’, the paper highlights the gaps that exist where the social and emotional understandings and political readings of frailty are concerned. The paper concludes by outlining the need to recognise the ‘politics of frailty’ including the power relations that are deeply embedded in the knowledge and practices surrounding frailty, and to incorporate older people's experience and ideas of vulnerability into research, policy and care practice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.269
Teacher spread0.249 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations15
Published2019
Admission routes2
Has abstractyes

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