Identity-Focused-Creation as a Catalyst for Language Teacher Preparation in the Post-Multilingual Era
Bibliographic record
Abstract
How can teachers working with plurilingual students conceptualize and implement pedagogical practices that reflect a postmultilingual view? How can they be empowered with the skills, knowledges, and sensibilities to rethink language education that prioritizes equity, inclusion, and social justice, ensuring that all students – regardless of their linguistic backgrounds – have the opportunity to succeed and thrive in a linguistically diverse world? This paper responds to these questions by embracing the power of fostering linguistic and cultural collaboration through the design of critical, creative, and collaborative multilingual and multimodal ( Prasad & Lory, 2020 ) teacher autobiographies. It brings together the voices of one scholar and three early years language educators. Based on the premise that transformation in language teacher education starts with critical self-reflexivity ( López-Gopar & Sughrua, 2023 ), we explore the power of art-infused autobiographies to help language teachers challenge and transform traditional, colonial approaches to language teaching by centring their experiences, perspectives, and voices. As we weave our narratives, we articulate the manner in which we embraced art-infused autobiographies, the affordances research-creation provided us, and the challenges we uncovered while carrying out identity-infused research-creation.
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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 | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.049 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| 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".