Stories of Racialized Internationally Trained Post-secondary Educators Re-entering their Professions
Bibliographic record
Abstract
This research project investigates the job search experiences of racialized internationally trained educators seeking to re-enter their professions in Canada. Previous studies have made extensive headway in understanding the job-search experiences of racialized immigrants. Specifically, some studies have demonstrated that racism is endemic to the Canadian labour-market, while others have concluded that work experience and credentials obtained in some countries are systematically undervalued in the Canadian labour-market. Further, studies have demonstrated that factors such as non English sounding names and accents can greatly limit some individuals’ job opportunities. Despite this widespread consensus, narrative accounts of job search experiences are almost entirely absent from present research. Hence, in distinction from the quantitative methods of the majority of recent studies of the subject, this work relies on the narratives of racialized immigrant educators for its principal empirical evidence. \nThe counter narratives assembled in this work provide a unique and unprecedented insight into the experience of racialized immigrant educators in the Canadian job-market. Through interviews with racialized immigrant educators from various educational, racial and political backgrounds, this study seeks to explore the challenges that are faced by some racialized immigrants in Canada. The results of this study confirm the consensus in the existing literature, but also demonstrate that discrimination against racialized immigrants has often been greatly under-stated. The narratives suggest that racialized immigrant educators experience significant discrimination during the job search process and in Canadian society in general. Further, this study reveals the extent to which the discrimination faced by racialized Canadian immigrants is not the result of single factors—such as race, accent, non English names and culture—but is rather the cumulative and overlapping result of multiple factors of discrimination. The consequences of this discrimination lead to alienation from Canadian society, family breakdown, disenchantment, loss of self-worth and identity. Subsequently the effects can extend from one immigrant generation to the next. These results are mostly unheard and unexplored in existing literature and dominant discourse.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 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 teacher head, 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".