Non-Native English-Speaking Teachers’ Legitimacy Negotiation in North American ELT Classrooms
Bibliographic record
Abstract
The purpose of this paper is to examine the complexities of non-native English-speaking teachers’ (NNESTs’) legitimacy negotiation process in North American English language teaching (ELT) classrooms. This paper explores how NNESTs’ processes and outcomes of legitimacy negotiation can be impacted by unbalanced power relations, assigned-identity, and human agency. By drawing on various sociocultural theories, particularly power relations (Bourdieu, 1977; Holland, Lachicotte, Skinner, and Cain 2001; Norton, 2000), identity (Norton, 2000; Harklau, 2000; Morita, 2004), and human agency (Canagarajah, 1999; Norton & Toohey, 2001; Lantolf & Pavlenko, 2001), the analysis examines how the three factors—unbalanced power relations, assigned identity and personal agency manifest themselves in NNESTs’ attempting to cross barriers erected by language, culture, and racial boundaries.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".