Partial recognition without redistribution: unpaid care in the devolved UK during COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic elevated care work as it was applauded on doorsteps and deemed ‘essential’ by governments. Yet this rhetorical visibility stood in stark contrast to its persistent structural invisibility. In the UK, women disproportionately shouldered the burden of social reproduction as healthcare workers, childcare providers, and unpaid carers, all while facing heightened job insecurity, domestic violence, and mental health strain. These patterns, mirrored globally, were exacerbated by policy responses that largely failed to recognise or support unpaid care. Feminist scholars have long shown how health crises reinforce gendered divisions of labour and marginalise unpaid care; this paper explores how that pattern was reproduced in the UK’s pandemic response, shaped by a decade of austerity and a residual model of care governance. Drawing on feminist political economy and critical policy analysis, this study compares how the four UK administrations – England, Scotland, Wales, and Northern Ireland – approached unpaid care across four domains of childcare, adult care, workplace flexibility, and public recognition. The analysis of policy documents reveals marked divergence: while Westminster leaned heavily on unpaid care with minimal support, devolved administrations adopted more redistributive measures, exposing the ideological and institutional logics that shape how care is valued in crisis.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".