The Nexus between the Racialized and Gendered Organization of Precarious Employment and the Lived Experiences of Racialized Immigrant Women in Canada: A Review of the Academic Literature
Bibliographic record
Abstract
Increasingly, highly educated racialized immigrant women are overrepresented in low-paying, low skill jobs that are situated in toxic work environments plagued by Ontario Employment Standards (ESA) violations and discriminatory employer practices. Various sites of oppression linked to socio-economic exclusion and class dislocation channel this population into racialized gendered professions that deepen their marginalization and increase employment precarity. This Major Research Paper consists of a literature review of peer-reviewed journal articles that specifically examine the lived experiences of racialized immigrant women in the Canadian labour market. Three themes emerged from the data: (1) labour market barriers, (2) social and structural barriers, and (3) labour market experiences. The results indicate that most studies focus on small groups of racialized immigrant women in one region vs. large-scale statistical analyses of various cities across Canada. Such an approach could provide pertinent information to help policy makers develop comprehensive labour market supports geared towards this population.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".