“I May Be a Native Speaker but I'm Not Monolingual”: Reimagining <i>All</i> Teachers' Linguistic Identities in <scp>TESOL</scp>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it