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An ‘all-world ageing’ perspective and its wider ethics of care: An empirical illustration

2024· article· en· W4401281619 on OpenAlexaff
Gavin J. Andrews, Megan Read

Bibliographic record

VenueSocial Science & Medicine · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScholarshipPerspective (graphical)CategorizationAgeingDisciplineSociologyFunction (biology)Environmental ethicsEmpirical researchEngineering ethicsSocial sciencePolitical scienceEpistemologyMedicineBiologyLawEngineeringComputer science

Abstract

fetched live from OpenAlex

Recent commentaries have proposed 'all-world ageing' as a new perspective for social scientific ageing research. It is based on the theoretical observation that the ageing process involves all forms of entities co-ageing relationally with each other, and with their surrounds. Its disciplinary implications hence being that what we categorize as ageing in social scientific ageing research should not be limited to human bodies, and that ageing non-humans should be brought under its purview. To empirically illustrate these theoretical and disciplinary assertions, and explore their implications, the current paper reports a study of how people co-age with non-humans they interact with in their daily lives. Sixteen people aged 66-90 were interviewed, ten of them also being observed at those times. The findings show some intricate and diverse relations that involve their co-ageing with varied biological entities and nature surrounds (such as plants, domestic animals and green spaces) and varied non-biological entities and non-nature surrounds (such as materials, technologies, accommodations, organizations and infrastructures). Meanwhile, important crosscutting themes - including lifespan, function and aesthetics - emerge as objectives of care, valued and exercised in broad terms. This empirical reconnaissance shows the potential for an all-world ageing perspective to engage diverse societal challenges and inform diverse areas of practice as part of a wider ethics of care. From it, a number of important considerations and undertakings arise for future scholarship.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.005
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.096
GPT teacher head0.478
Teacher spread0.382 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2024
Admission routes1
Has abstractyes

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