Precarious Work Experiences of Racialized Immigrant Woman in Toronto: A Community- Based Study
Bibliographic record
Abstract
Despite their high levels of education, racialized immigrant women in Canada are over-represented in low-paid, low-skill jobs characterized by high risk and precarity. Our project documents the experiences with precarious employment of racialized immigrant women in Toronto. We conducted 30 semi-structured interviews with racialized immigrant women. Participants were recruited through posted flyers, partner agencies, peer researcher networks and snowball sampling. Interviews were transcribed and analyzed using NVivo software. The project followed a community-based participatory action research model. Participants faced powerful structural barriers to decent employment and additionally faced barriers associated with household gender relations. Their labour market experiences negatively impacted their physical and mental health as well as that of their families. These problems further constrained women’s ability to secure decent employment. Our study makes important contributions in filling the gap on the gendered barriers racialized immigrant women face in the labour market and the gendered impacts of deskilling and precarity on women and their families. We propose labour market reforms and changes in immigration and social policies to enable racialized immigrant women to overcome barriers to decent work.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".