The Decolonizing Potential of Creative Autobiographies in Exploring Plurilingual Teacher Identities
Bibliographic record
Abstract
ABSTRACT The call for second language teacher education (SLTE) to embrace teacher identity is not new. There is a growing body of research that theorizes teacher identity, focuses on the centrality of teacher identity in teacher learning and professional growth and highlights the impact of teacher identity on their pedagogical practices. However, little is known about how teacher education can integrate identity‐oriented practices into their programs or the impact of these practices on teachers. Building on Yazan's (2018) call to infuse autobiographical work to foreground teacher identity, this paper reports on an autobiographical creation activity aimed to help teachers reflect on their identities using creative means carried out in a graduate‐level Teaching English as an Additional Language (TEAL) program. It brings together the voices of the teacher educator who introduced the autobiographical creation project and two graduate students who created autobiographies. They critically discuss the core elements of the project, provide concrete examples of autobiographical creations, the processes involved in the creation and the challenges encountered as educators and graduate students in implementing and constructing the creativity‐induced autobiography. This critical reflection brings to the foreground the decolonizing potential of creative autobiographies in helping plurilingual teachers explore their identities.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".