Immigration Policy and the Live-in Caregiver Program: The Racialization of Feminized Work in Canada's Labour Market, an Intersectional Approach
Bibliographic record
Abstract
Immigration policy in Canada has increasingly been relied upon in order to meet shortterm economic objectives, while conversely, immigration outcomes have increasingly continued to decline. Programs such as the Temporary Foreign Worker Program and the Live-in Caregiver Program (LCP) were created in order to meet labour market shortages with a more flexible labour force. The potential of achieving permanent residency status through these programs provided the incentive for migrants to participate. However, the temporary status of migrants and the precariazation of their employment in a flexible labour market has contributed to increased levels of poverty, underemployment or unemployment, inequality, and social exclusion amongst Canada’s immigrant population, with a disproportionate representation of women. This paper uses an intersectional framework to analyze the LCP as feminized work, revealing the intersecting inequalities of immigration status, gender, labour market participation, and racialization. The systemic exploitation and barriers to integration experienced by many migrants, premised on these intersecting identities, are inimical to Canada’s long-term social and economic objectives. A comprehensive analysis of immigration policy and the LCP is explored in this paper, concluding with policy recommendations to address the poor working conditions of the LCP and the lack of support for migrants in the integration process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.016 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".