“We are in our country. Why do we have to resort to western ways of doing things?”: an analytic framework for knowledge application in language teachers studying abroad
Bibliographic record
Abstract
Abstract In this article we suggest a framework for researching the study abroad experiences of English language teachers, and analyze data from a study of higher education English teachers from four Southeast Asian countries who completed graduate studies at a Canadian university. We present data from interviews with 10 participants which took place in their home countries four years after taking the program. Research questions and interview questions relate to the relevance of the program to participants’ professional practice, the challenges they faced when applying knowledge in their local contexts, and the opportunities and benefits that came with completing a Canadian graduate program. In our data analysis, we highlight a combination of local, national, and transnational factors, as well as cultural differences, that affected the application of knowledge learned in Canada. We conclude by considering a number of implications for educators involved in study abroad programs for English language teachers.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".