Critical language teacher identity
Bibliographic record
Abstract
Language teachers have certain ideological or political inclinations, ranging from neutral, conservative, liberal, to radical. Although my ideological position during the early years of my teaching career-teaching English as a foreign language in public schools in Japan-was neutral, I gradually integrated socially relevant and critical perspectives into my teaching, perhaps because I had always been supportive of social justice while I was growing up. During my doctoral work in Toronto, Canada, in the early 1990s, I was introduced to critical pedagogy and critical applied linguistics through coursework and informal discussions with peers. Since then, I have taken critical perspectives in teaching Japanese as a foreign language and language teacher education in North America. However, a classroom incident, which happened a few years ago, provided me with an opportunity to critically refl ect on the ways in which critical pedagogy had been implemented in my classroom (Kubota, 2014). This enabled me to further critically refl ect on my teacher identity as a critical pedagogue especially with regard to my ideological positioning vis-à-vis my students’ and the ways in which I had engaged students in what I regard to be critical perspectives. Drawing on this experience, I will discuss issues of identity relating to language teachers and language teacher educators who support critical approaches to pedagogy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".