Non-native English–Speaking Teachers' Negotiations of Program Discourses in Their Construction of Professional Identities within a TESOL Program
Bibliographic record
Abstract
The professional identity of language teachers has gained prominence in research on language instruction in the last decade. This article adds to work by critically exploring how teacher education programs allow non-native English–speaking teachers (NNESTs) to construct positive professional identities and become pro-active educators. It reports on a study of the discursive constructions of professional identities that 20 NNES pre-service teachers developed within a Master of Education TESOL program for international students in a Canadian university. Drawing on post-structural and sociocultural understandings of identity, the article offers a Bakhtinian analysis of the negotiations and dialogical appropriations of authoritative program discourses that these pre-service NNESTs reflected upon in portfolios summarizing their learning in the program. The article concludes by describing the implications of this research for TESOL and cost-recovery international programs in British, Australian, and North American universities.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 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".