"I'm not working, but I'm a professional": Precarious employment pathways, consequences, and resistance strategies impacting the health of racialized immigrant women
Bibliographic record
Abstract
Precarious employment, which is characterized by low wages, insecurity, few entitlements, and a higher risk of ill health, is on the rise in Canada; however, precarious forms of employment are not equally distributed across society, but are borne disproportionately by some groups, including racialized immigrant women. To better understand the impact of precarious employment on the health of racialized immigrant women, I conducted a qualitative study consisting of semi-structured interviews (n=21) and two follow-up focus groups (n=11) with women in Southwestern Ontario. The women were recruited through a combination of convenience and snowball sampling and were compensated for their time as well as childcare and transportation costs. With this doctoral study, I explored the pathways between precarious employment and women’s health, as well as the associated consequences. My study was guided by feminist and constructivist grounded theory methodologies and informed by both intersectional feminist and social determinants of health perspectives. With this critical lens, particular attention was paid to the intersection of gender, race, class, and immigrant status in relation to precarious employment and health. Further, data were collected and analyzed using an iterative and reflexive approach and guided by feminist research ethics. Within this dissertation, I present the findings from this study including direct and indirect pathways between precarious employment and health, non-employment related health and safety risk factors, and strategies employed to resist precarious employment. In addition, I discuss the findings in relation to relevant literature, as well as several policy considerations and areas for future research.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".