Using a Book-Club Model to Support Racial Literacy Development among Teachers of Multilingual Learners
Bibliographic record
Abstract
New teachers need time, support, mentorship, and experience to build racial literacies that will transform teaching. In response, this article explores the potential of book-club–style professional development to promote racial literacy among “mainstream” teachers of multilingual learners. In presenting a qualitative inquiry of participants in an international, intergenerational, action-oriented racial literacy book club for teachers of multilingual students, we share three findings regarding how the book club functioned to support new teacher participants to grow their racial literacy and antiracist awareness for addressing injustices in their various spheres of influence: (1) the book club created an extended learning environment for new teachers to grow their racial literacy in tandem with their developing classroom practice; (2) it fostered understanding among participants living and teaching in different parts of North America regarding the ways in which issues of race and language were both common across contexts and locally inflected; and (3) it supported the development of mentoring relationships. We conclude with a discussion of implications for the broader use of book clubs as part of socially conscious teacher education and professional development.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".