THE EXERIENCES OF IMMIGRANT LIVE-IN CAREGIVERS IN ONTARIO
Bibliographic record
Abstract
This thesis aims to fill in the gaps in our knowledge of the experiences of immigrant live-in caregivers in Canada as a means to better understand the role of social networks and how this is informed by intersectionality theory. More specifically, based on 34 qualitative interviews with current and former live-in caregivers, this dissertation explores the migration, working, living and integration experiences of immigrant live-in caregivers in Ontario, Canada. In particular, I focus on: 1) their experiences with processes of decision making, migration and finding a job with a particular focus on role of social networks in these processes; 2) the impact of the type of care on live-in caregivers’ working and living experiences as well as the role of networks in the process of their adaptation to life in Canada; and 3) their integration experiences (with a particular focus on role of social networks in these processes). Although some researchers relied on some sociological perspectives in their studies focusing on live-in caregivers in Canada, none of the previous studies used these in conjunction with social network theory. My research shows that while social network theory is useful in considering the role of social networks in migration, living and integration experiences of LCP workers, it is not sufficient to come to a complete understanding of these issues. On the basis of my findings, I conclude that one should combine social network theory and intersectionality when exploring such issues with regards to not only LCP workers in particular, but also immigrant workers in general. By bringing such new empirical and theoretical insights, my dissertation contributes to the body of previous research on experiences of LCP workers in Canada and more broadly to the literature on domestic workers in an international context, immigration research focusing on social networks and sociological theory.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".