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Record W4224250308 · doi:10.3384/ijal.1652-8670.3499

Ageing, embodiment and datafication: Dynamics of power in digital health and care technologies

2022· article· en· W4224250308 on OpenAlexafffund
Nicole Dalmer, Kirsten Ellison, Stephen Katz, Barbara Marshall

Bibliographic record

VenueInternational Journal of Ageing and Later Life · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsTrent UniversityMcMaster University
FundersCanadian Institutes of Health Research
KeywordsPrecarityPower (physics)TechnoscienceSociologyGerontechnologyDynamics (music)Health careTechnological changeFutures contractInequalityGender studiesTemporalitiesBiopowerPoliticsSocial sciencePolitical scienceEconomic growthEconomics

Abstract

fetched live from OpenAlex

As a growing body of work has documented, digital technologies are central to the imagining of aging futures. In this study, we offer a critical, theoretical framework for exploring the dynamics of power related to the technological tracking, measuring, and managing of aging bodies at the heart of these imaginaries. Drawing on critical gerontology, feminist technoscience, sociology of the body, and socio-gerontechnology, we identify three dimensions of power relations where the designs, operations, scripts, and materialities of technological innovation implicate asymmetrical relationships of control and intervention: (1) aging bodies and the power of numbers, (2) aging spaces and the power of surveillance, and (3) age care economies and gendered power relations. While technological care for older individuals has been promoted as a cost-effective way to enhance independence, security, and health, we argue that such optimistic perspectives may obscure the realities of social inequality, agist bias, and exploitative gendered care labour.

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 score0.485
Threshold uncertainty score0.196

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 studies0.0000.000
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 head0.011
GPT teacher head0.286
Teacher spread0.275 · 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

Citations38
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Ageing and Later LifeSame topicTechnology Use by Older AdultsFrench-language works237,207