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Record W4414610992 · doi:10.3390/soc15100271

Working from (a New) Home: Tensions Faced by Remote Working Immigrants in Canada

2025· article· en· W4414610992 on OpenAlexafffundabout
Samantha Jackson, Suzanne Huot

Bibliographic record

VenueSocieties · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
FundersCanada First Research Excellence Fund
KeywordsImmigrationThematic analysisSocializationFlexibility (engineering)ReflexivityWork (physics)FeelingPrecarious workWorkforce

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.236
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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