Racialized Workers in White Spaces: Exploring the Experiences of Racialized, Immigrant Care Workers in Rural and Small Town Canadian Long-Term Care Homes
Bibliographic record
Abstract
What are the everyday life experiences of racialized immigrant care workers in rural and smalltown Canadian long-term care homes, and how do they navigate white spaces in these contexts?This pre-Covid study centers the experiences of immigrant care workers and describes how they navigate white dominance in rural and small-town Canadian long-term care homes.The study is timely and provides relevant implications for long-term care homes, pandemic and beyond.While several authors have offered excellent scholarship on long-term care home work and immigrant, racialized workers, the focus has been mainly on urban centers.As a result, little is known about their conditions of work and care in rural and small-town long-term care homes.My research contributes to extant research by bringing into focus the experiences of racialized care workers in rural and small-towns and demonstrating how white dominance exacerbates poor conditions of work for immigrant care workers.The study utilizes feminist political economy, critical race theory and critical whiteness theory as theoretical foundations, to center their voices in care processes.The analysis draws on data collected in a sub-study of the Seniors' Adding Life to Years (SALTY) project, in which rapid team-based ethnographies were conducted in rural and small towns in Ontario, Nova Scotia, and British Columbia.The study analyses white environments and demonstrates how white-settler culture is woven into long-term care spaces, excluding racialized immigrants.Further, it draws on auto-ethnographic stories from my experiences as a racialized immigrant in the research field and stories from immigrant care workers to explain processes of racialization in long-term care home settings.Parsing out the implications of the casualization of long-term care home labour and burdensome credential validation processes, the study demonstrates how immigrant care workers are set up to experience the most precarious conditions of work in long-term care homes.To my committee, I say a big thank you for shaping my ideas from the onset and for challenging me to do better.Thank you, Colleen Lundy, Lisa Mills, and Paul Mkandawire. My sincere gratitude to the Seniors Adding Life to Years (SALTY) principal investigator, Janice
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.016 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".