Global Care Chains, Employment Agencies and the Conundrum of Jurisdiction: Decent Work for Domestic Workers in Canada
Bibliographic record
Abstract
Transnational migrant domestic workers who perform work on a live-in basis in Canadian homes cross a number of jurisdictional boundaries – between different nation states, different areas of law, and different levels of government within a nation. This “transgression” makes it difficult to govern these workers in a manner that provides decent work. The goal of this article is twofold: first, to explain how the movement of transnational migrant workers across a number of nested jurisdictional boundaries makes them susceptible to being exploited; and second, by focusing on the role of the transnational intermediary – the broker or employment agency that places domestic workers across national boundaries to live and work in the homes of Canadian families – to show how it is possible to overcome some of these jurisdictional conundrums. The article begins with a discussion of the key concepts – global care chain and jurisdiction – that are used to examine Canada’s Live-in Caregiver Program and the regulation of employment agencies in British Columbia in order to illustrate how the jurisdictional conundrum creates problems for achieving decent work for domestic workers. It concludes by suggesting some ways to resolve the jurisidictional conundrum and, drawing upon existing Canadian models, sketches some techniques to regulate employment agencies with the goal of promoting decent work for migrant domestic workers.
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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 | 0.029 | 0.022 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".