Help wanted: the employment trajectory of multilingual transnational teachers
Bibliographic record
Abstract
Even though Canada is known as a multicultural place that embraces immigrants from all over the world, it can be challenging for immigrants to make their way not only in, but into, the work force. For immigrant teachers, who have non-Canadian education and, many times, an accent that may be unfamiliar to locals, it can be even more difficult. This study explored the employment trajectory of multilingual transnational teachers. On the basis of my findings, I provide insights and recommendations that might be useful for other professionals who may be experiencing similar challenges joining the labor market in Canada. The autoethnographic approach used in this study allowed me to share authentic experiences and invite the readers to enter my world as a multicultural transnational educator and see it from my perspective. I reviewed the literature in the areas of Immigrants in the Canadian workplace, native and non-native English-speaking teachers (NESTs/NNESTs), and relevant challenges associated with the language teaching profession. Data analyzed consists of reflections of memories of my own employment trajectory. Ellis and Bochner (2000) state that examining someone’s own experiences can lead to a better understanding of a culture. This study showed that NNESTs are still affected by the belief that NESTs are the best language teachers (Llurda, 2005; Selvi, 2010: Ulate, 2011). During my path to integrate into the Canadian labour market, like many of my contemporaries, I faced challenges such as non-recognition of my foreign education credentials and home country experience, linguistic discrimination, and undervaluing of NNESTs on the part of students and employers. Such challenges create obstacles for NNESTs to find teaching positions and impede their professional success.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".