Forging a Path for Diverse Identities and Voices: Multilingual Educators’ Reflective Stories
Bibliographic record
Abstract
Shifting attitudes and requirements for refugee and visa applications in Canada, executive orders on immigration and language policies in the United States, and legislation against Critical Race Theory have contributed to changing the educational landscape of North America (Banerjee et al., 2025; Bumgardner et al., 2025; Government of Canada, 2025; Immigration, Refugees and Citizenship Canada, 2024; The White House, 2025; World Population Review, 2025). This paper draws on four multilingual educators’ personal stories, narratives, and reflective inquiries as language learners to explore layered identities in education. Shelley K. Taylor introduces how linking race, language and identity work through stories help us relate, learn, and change (Wink, 2017). Next, her four co-authors share their individual stories, beginning with Ryan Pontier. He shares his bilingual journey in which he contrasts his situated experiences with those of his students while highlighting the importance of translanguaging and equitable educational practices for multilingual students. Ryuko Kubota challenges normative expectations of "native-like" proficiency and advocates for justice-affirming education. David Schwarzer examines the historical use of languages in English Language Teaching, evolving attitudes towards multilingualism, and making space for translingualism as a linguistic phenomenon, a pedagogy, and an ideology. Lastly, Melissa Rivera Screven discusses the fluidity and hybridity of language use among multiply-marginalized multilingual families, showcasing a translingual-vision approach for the future of English language teaching. These narratives provide insights into how race, languaging and identities shape educational experiences, and emphasize the importance of a gradient array of diverse perspectives. This paper situates the critical need to prepare teachers to persevere in the profession and envision a more inclusive, equitable future for all learners.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.036 | 0.050 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".