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Conceptualising and measuring the social care economy

2025· article· en· W4409874533 on OpenAlexaff
Norah Keating, Jane Badets, Fabio Robibaro

Bibliographic record

VenueJournal of global ageing. · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of TorontoStatistics CanadaUniversity of Alberta
Fundersnot available
KeywordsSocial economySocial careBusinessEconomicsMarket economyMedicineNursing

Abstract

fetched live from OpenAlex

The concept of the care economy has garnered considerable international attention. Social justice arguments about decent work, the gendered nature of care work and the need to account for care, along with critiques of gross domestic product as an adequate metric for the wellbeing of nations, have all informed a call to place care on national agendas. The language of ‘care crisis’ underscores the urgency of accounting for care and for determining the social contract between society and family in the responsibility for providing care to those who are most vulnerable. United Nations agencies have called for the development of systems of long-term care, noting that families should not be held responsible for care. In this article, we present a framework for the social care economy that aims to make care work visible. Care work remains undervalued in our societies and economies, and its workers often remain marginalised. We define the social care economy as that sector of the broader economy comprising paid and unpaid work provided to those needing assistance with daily functioning: young children, younger people with chronic conditions and disabilities, and older people with chronic conditions and disabilities. We specify data needs and identify gaps in determining the balance of state versus family responsibility for care and in documenting and making visible the work of paid and unpaid carers. We conclude with a discussion of how the framework might lead to insights into the wellbeing of carers and of the nations in which their care work is embedded.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score1.000

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 studies0.0020.000
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 head0.025
GPT teacher head0.350
Teacher spread0.324 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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