Re-skilling, up-skilling, and the devaluing of skill : skilled immigrant women language teachers' lived experience of identity
Bibliographic record
Abstract
This study explores the lived experiences of skilled immigrant women language teachers in Canada through feminist intersectional and new materialist frameworks, seeking to understand the intricate dynamics of identity, power relations, and inequities. Women-identified skilled immigrants are more likely to be overeducated than their male counterparts and have higher levels of education than Canadian-born women. However, they experience higher unemployment rates, compounded by racial, gender, and linguistic biases. While considerable research considers ESL learners, the experiences of immigrant women language teachers—Canada’s largest group of language educators—remain understudied. Using a feminist intersectional approach, the study examines how identity categories (e.g., gender, race, ethnicity, immigration status, second language, and accent) and material-discursive factors (e.g., physical appearance, dress, and age) affect these women’s identities and lived experiences. New materialist frameworks emphasize how non-human entities (e.g., objects and pets) are vital in shaping these women’s identities. Data were collected through individual interviews, artifacts, and photo-elicitation from 17 participants of diverse backgrounds and demonstrated how immigrant women language teachers face significant challenges, such as systemic discrimination, underemployment, re-credentialing and professional reintegration, financial constraints, and insufficient government support. The findings point to objects (e.g., coins, jewellery, and religious icons) serving as material agents that connect these women to their past, empowering them while negotiating their identities in Canada. Furthermore, as non-human vital participants, pets become integral to inter-species heterogeneous family assemblages, offering mental and emotional support, and helping the women navigate challenges as immigrants. This research contributes to understanding systemic inequities in the Canadian workforce, emphasizing the need for ongoing research to address the unique barriers faced by racialized, skilled immigrant women language teachers. Employing feminist intersectional and new materialist approaches, this study sheds light on underexplored material and emotional dimensions in the 17 women’s lived experiences while challenging more traditional human-centred analyses of immigration and identity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.021 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".