Exploring the Professional Identity and Professional Agency of Non-native English–Speaking Teachers within TESOL Programs in Canada: An In-depth Literature Review
Bibliographic record
Abstract
Identity and agency play pivotal roles in shaping teachers' commitment, involvement, and personal autonomy throughout their professional growth journey. This literature-based thesis seeks to explore the creation of transformative professional learning experiences to foster the positive growth of non-native English-speaking teachers’ (NNESTs) identity and agency. It begins by addressing challenges in defining NNEST's professional identity and reevaluating current notions of NNEST's growth. Shifting the discussion to NNEST growth, the thesis examines the interconnectedness of identity and agency. This study argues for a broader understanding of transformative learning as a theoretical framework for supporting NNESTs. Drawing on transformative learning theory, the thesis suggests strategies for facilitation methods that empower NNESTs to realize their full potential in international education programs, particularly in TESOL (Teachers of English to Speakers of Other Languages). The research findings shed light on the negotiation of linguistic and cultural aspects, challenges faced by NNESTs, the role of discursive constructions, and teachers' emotional responses. Demonstrating how professional development can facilitate transformative learning, the study highlights the importance of creating supportive environments that encourage critical reflection and validate multiple identities. It also discusses implications for promoting positive identity construction and professional integration.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".