Precarious Work Experiences of Racialized Immigrant Woman in Toronto: A Community- Based Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it