Centering Multilingual Learners and Countering Raciolinguistic Ideologies in Teacher Education: Principles, Policies and Practices
Bibliographic record
Abstract
In Centering Multilingual Learners and Countering Raciolinguistic Ideologies in Teacher Education, Jeff Bale and colleagues thoughtfully weave narratives set to disrupt the inequality and racism that have often gone unaddressed in language teaching and teacher training.The book offers a timely and insightful exploration of the intersection of language, race/racism, and teacher education, drawing on rich empirical research and theoretical frameworks.However, the research does not launch straight into the connection between racism and language.Inevitably, the doing of the research guides the analysis toward exploring this aspect.The theme of "collective thinking" (p.xv) permeates throughout, and the authors recognize that "this text and the project behind it are the products of a significant and rewarding group effort" (p.xv).Woven throughout the book are the researchers' reflective stances, vignettes, and brief profiles of participants.These elements serve distinct functions: The reflective stances provide personal insights, the vignettes illustrate specific instances, and the profiles offer a deeper understanding of the participants.Together, they connect the thinking and doing of those involved in the research to who they are "as entire people and not just as (future) teachers" (p. 5) or mere participants in a study.This multifaceted approach enriches the narrative by highlighting the diverse identities and experiences of both the researchers and the participants, and it enhances the book's overall depth and authenticity.This book stands to be advantageous to various reader demographics, including educational researchers, policy makers, English instructors, teacher trainers, and curriculum developers.The author statements remind the reader of our identity as homo narrans, and the traditional "author bios" section is replaced by a picture of the author team with each author introducing themself "in terms of who we are as people, how we came to this research, how our social positioning shaped our approach to the study, and what we have learned or how we have changed through doing this work" (p.xvii).The authors remain consistent with the theoretical positioning of this book, as each one takes the lead in "telling the story of this project" in different chapters.The book is theoretically positioned at the intersection of race, language, and teacher education, thus drawing from raciolinguistics and Critical Race Theory.The co-constitution of race and language is emphasized, framing them as intertwined social constructs rather than separate entities.The authors critique traditional cognitive approaches to language education, advocating instead for an understanding of multilingualism that includes social, historical, and material dimensions.They argue for a shift in focus from individual teacher beliefs to systemic structures of white supremacy within educational institutions.By foregrounding raciolinguicized subjectivities (p.67), the book highlights the need for teacher education programs to address the dynamics of race and language in preparing future educators to effectively support multilingual learners.The book comprises eight chapters, the first three of which carefully work together to ascertain what and whom the book is about.In Chapter 1, "Contradictions of Stability and Change," Bale and Gagné begin
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.038 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".