Bridging the Gap: Exploring the Experiences of South Asian Women Immigrant Teachers in Toronto
Bibliographic record
Abstract
In this dissertation, I share the voices of South Asian women immigrant school teachers living in Toronto. In this era of global mass migration and the increasing number of women immigrants, I argue that it is important to examine how gender and race affect racialized immigrant women’s working experiences. Historically, racialized immigrant women in Canada have faced various forms of discrimination in the labour market: not only are their previous qualifications and experiences devalued in the job market, but after entering the job market, racial and gender identity remain a concern in their professional lives (Crea-Arsenio et al. 2022; Premji et al. 2014). While scholars have highlighted the common labour market barriers, the struggles of South Asian women when facing these challenges in seeking a specific career do not get enough attention in the academic world. A significant number of South Asian women must engage in precarious jobs that are not consistent with their skills and qualifications. Here, I recruited South Asian immigrant women who hold a teaching certificate in Ontario and are coping with the secondary-education labour market and/or other related jobs in Toronto. Guided by a Critical Race Feminist perspective, I used interpretive inquiry as a research methodology to facilitate participants telling their struggles, challenges, and negotiations of their everyday lives while living in a large urban center like Toronto. My analysis of this research shows that these South Asian groups of women must overcome barriers that are similar to many other non-racialized female professional immigrants - but as racialized female immigrants, they also face more challenges in accessing and coping with their current professions. My findings suggest that policymakers should focus on an adaptable labour-market transition process for these professional groups after migration. This could also be helpful for other racialized groups in general. Promulgation to eliminate systematic barriers through various forms is needed to decrease the substantial existence of teacher diversity gap in Ontario. Therefore, this study extends the available literature by considering voices of racialized immigrant women, thereby addressing some existing gaps in policy framework.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".