Conceptualising and measuring the social care economy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".