How a Non-Native Speaker Constructs Positive Identities in a Master’s Teacher-Training Program in Canada
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
In this paper on how a non-native English speaker (NNES) constructs positive identities, I argue that a Master’s teacher-training program in Canada has offered me resources, support as well as space to develop my own complex identities (Norton & Toohey, 2011). Speaking from the perspectives of a NNES, I aim to encourage pre-service or in-service teachers to think positively of themselves with my personal anecdotes. I first discuss constructs of Norton & Gao’s (2008) identity and investment, and how my identity has been (re)shaped in the particular sociocultural context in a Canadian university. My investment in the current program does not just help me improve the target language, but rather increase my cultural capital. Then, I analyze Bakhtin’s dialogism (as cited in Johnson, 2014), and relate the concept to illustrate the significance of engaging myself in a dialogue with peers and professors, and how everything people say or do has a meaning in relation to others. Lastly, I address the notions of interactive others (Kettle, 2005) along with multicompetence (Cook, 1996, as cited in Block, 2003). Interactive others provide audible space for people to be heard, and how they have made a difference in my life. As a NNES, I am not a failed monolingual, but a multicompetent language user who has knowledge of not just one language in my own mind (Cook, 1996). I hope to bring positive influences on those who will enter the job market soon.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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