“I May Be a Native Speaker but I'm Not Monolingual”: Reimagining <i>All</i> Teachers' Linguistic Identities in <scp>TESOL</scp>
Bibliographic record
Abstract
Teacher linguistic identity has so far mainly been researched in terms of whether a teacher identifies (or is identified by others) as a native speaker (NEST) or nonnative speaker (NNEST) (Moussu & Llurda, 2008; Reis, 2011). Native speakers are presumed to be monolingual, and nonnative speakers, although by definition bilingual, tend to be defined by their perceived deficiency in English. Despite widespread acceptance of Cook's (1999) notions of second language (L2) user and multicompetence, and despite major critiques of the concept of the native speaker (Davies, 2003; Hackert, 2012), the dichotomy lives on in the minds of teachers, learners, and directors of language programs worldwide. This article sets out to show that the linguistic identities of TESOL teachers are varied and complex, and that the dichotomy does little justice to this complexity. Findings are reported from the linguistic biographies of 29 teachers of adult TESOL in seven countries, and a detailed account is given of the rich linguistic identities of two of those teachers, one in Japan and one in Canada. The findings bear out those from Ellis (2013) undertaken in the Australian context. The article concludes with a call for recognition of the plurilingual multicompetencies of all TESOL teachers, and for these identities to be valued in the context of the TESOL classroom to assist learners who are becoming plurilingual.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| 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".