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Vulnerabilities in Paid Care Work

2025· book· en· W4414513010 on OpenAlexaboutno aff

Bibliographic record

VenuePolicy Press eBooks · 2025
Typebook
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCare workContext (archaeology)Psychological interventionActive listeningUnpaid workPaid workWork (physics)Qualitative research

Abstract

fetched live from OpenAlex

This book reports on qualitative case studies of the everyday lives and experiences of paid care workers during and after the COVID-19 pandemic, in four countries – Canada, Finland, South Africa and the UK. While care work is often praised as morally admirable, in these countries – and many others – it remains low-paid, low status and with poor working conditions. In Cape Town, South Africa, the research looked at the experiences of paid ‘domestic workers’, who play an important role in caring in the home. The Canadian study drew on the experiences of long-tenure care workers in Ontario, while the Finnish study examined the experiences of LGBT and ‘migrant/foreign-born’ care workers. The UK study considered domiciliary care workers, employers and recruitment agencies in the south-east of England. After an introductory chapter setting out the main themes of the approach, in the four main empirical chapters the findings from case studies are presented, paying attention to the intersecting differences of gender, age, sexualities, localities, ‘race’/ethnicity and economic and social positions. The final chapter draws together what was learned from listening to the voices of care workers in these different contexts, placing conclusions in the context of intersectional inequalities and disempowerment as well as local and global disparities, and highlighting the significance of hearing care workers’ voices. This chapter discusses four interlinked interventions to improve care workers’ working lives and enhance policy thinking and decisions: unionisation; legal and organisational protections; changing public perceptions; and professionalisation and training.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.303
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.002
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 head0.083
GPT teacher head0.422
Teacher spread0.339 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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