Cultivating Teacher Identity in a Graduate Program: A Holistic Approach
Bibliographic record
Abstract
Engaging with recent calls to incorporate teacher identity as a central principle in language teacher education, this article aims to address practical ways to support teacher identity development in students in a graduate program for language educators. Employing duoethnography as a qualitative research approach and reflective practice, the two authors, who are instructors in the program, engage in conversation on coursework and activities that invite reflection on and negotiation of identities among participants in the program. The work we have been doing explores a variety of aspects to create a more holistic lens from which to support the development of teacher identity as connected to professional identities (educational beliefs, practices, and experiences) and personal identities (cultural background, ethnicity, language, gender, etc.). The idea that who we are is continuously evolving in a process of becoming is a metaphor guiding identity work in the program. This process of becoming and teaching who we are calls for teacher educators to consider in depth the impact of teacher education activities and processes on student teachers’ developing understandings of themselves as language educators in our globalized world.
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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 | 0.004 | 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 | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".