The Remote Work Mirage: How Digital Labour Markets Reinforce Inequality for Racialized Immigrant Women
Bibliographic record
Abstract
Remote work is often promoted as a flexible, democratizing force in the labour market. Yet for highly skilled racialized women—particularly immigrant women—this shift has not dismantled entrenched structural barriers. Instead, it has frequently reproduced them in digital form. This paper critically examines how race and gender intersect to shape access to remote employment, advancement, and economic security, both globally and in the Canadian context. Drawing on an intersectional framework, labour market segmentation theory, and scholarship on algorithmic hiring bias, it interrogates whether remote work mitigates or reconfigures pre-existing inequalities. The analysis shows that racialized immigrant women remain disadvantaged in digital hiring systems, underrepresented in leadership roles, and disproportionately burdened with unpaid care work—constraints that the remote work model has failed to resolve. The paper argues that far from being a meritocratic leveller, remote work can entrench a digitally mediated extension of existing inequalities unless deliberate structural reforms are enacted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".