Linguistic barriers among Internationally Educated Teachers in Ireland and Canada
Bibliographic record
Abstract
Drawing on qualitative interview and focus group data collected from Internationally Educated Teachers (IETs) in the context of two different research studies conducted in Ireland and Manitoba, Canada, this article critically examines how national/regional linguistic requirements and expectations of a hidden curriculum are experienced as barriers to employment and as status inhibitors. While the two sites contrast starkly in terms of size, migration frameworks, and population demographics, some IETs are faced with comparable barriers in terms of securing work in their field in both countries. We make two main arguments on the basis of our findings: 1) language proficiency requirements for IETs should be accompanied by appropriate language supports, and 2) education systems must move beyond viewing language within a monolingual framework to avoid devaluing the rich linguistic repertoires of IETs who are multilingual. Analysis draws on the concept of plurilingualism to advocate for a more complex and inclusive approach to defining linguistic competence for teachers. Further, the theoretical lens of language ecology usefully emphasises the wider linguistic context that should be taken into account when designing and implementing policy and programming for IETs. Implications of this research illuminate the ways power and linguistic identity intersect in international education systems.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".