How a Non-Native Speaker Constructs Positive Identities in a Master’s Teacher-Training Program in Canada
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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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 | 0.034 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".