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Record W6911170891 · doi:10.5167/uzh-151186

At home with the boss: Migrant live-in caregivers, social reproduction and constrained agency in the UK, Canada, Austria and Switzerland

2018· article· en· W6911170891 on OpenAlexaboutno aff

Bibliographic record

VenueZurich Open Repository and Archive (University of Zurich) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNexus (standard)Agency (philosophy)Care workReproductionContext (archaeology)Social reproductionFactoringUnpaid work

Abstract

fetched live from OpenAlex

In many countries of the global North, families increasingly rely on live-in care-givers to look after their children and elderly. Although much care work remains unpaid and informal, several states have set up a variety of migration and labour regimes to guarantee a steady supply of workers to provide paid live-in care in the home. This paper contributes to a broadening of the focus of labour geography beyond “productive” labour by factoring in the theoretical perspective of social reproduction into the debates on constrained agency. Our aim is to identify the mechanisms that make these regimes function for employers and employees, and their consequences for the social reproduction of the workers. To do so we compare live-in care schemes in the UK, Canada, Austria and Switzerland and examine the ways in which live-in care is differentially institutio nalised. Our policy analysis in these four countries shows that the constrained agency of the workers does not solely stem from their status as migrants, but is produced by the nexus of specific migration, care and gendered labour regimes. Furthermore, we argue that we need to extend our perspective beyond the immediate work context to see how live-in care regimes not only infringe, but also enable, the social reproduction of the workers – a fact that has often been neglected by existing research.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.011
GPT teacher head0.210
Teacher spread0.200 · 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 designObservational
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

Citations2
Published2018
Admission routes1
Has abstractyes

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