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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".