Working from (a New) Home: Tensions Faced by Remote Working Immigrants in Canada
Bibliographic record
Abstract
Remote work has become a prevalent work model in Canada but there is limited research on how immigrants experience this type of work. This paper shares thematic findings from an instrumental case study that explored such experiences in two of Canada’s largest immigrant-receiving provinces. We interviewed 21 highly educated remote workers from the Global South who had immigrated to Canada in the last 10 years. We generated three themes from a reflexive thematic analysis of interview transcripts. (1) Shifting labour market value: despite enhancing their skillsets, many participants still faced labour market devaluation, which led to feelings of insecurity. (2) Occupational flexibility: participants enjoyed the flexibility remote work provided but often could not draw distinct boundaries between work and home. (3) Socialization and belonging: being physically removed from the workplace affected participants’ connections with others within and outside of the workplace. Participants viewed hybrid work as a possible solution for achieving better balance. Using a Bourdieusian lens, we conceptualize the Canadian job market as a site where social inequalities are reproduced by employers and immigrants. We suggest that greater governmental and workplace support systems for socialization, integration, mentorship and building cultural awareness could help immigrants better achieve their career goals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.041 | 0.013 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".