At home with the boss: Migrant live‐in caregivers, social reproduction and constrained agency in the <scp>UK</scp>, Canada, Austria and Switzerland
Bibliographic record
Abstract
In many countries of the global North, families increasingly rely on live‐in caregivers 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 institutionalised. 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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".