Critical teacher education for equitable learning in multilingual classrooms: a possible way forward
Bibliographic record
Abstract
Addressing the ongoing calls to reform teacher education to prepare future teachers to serve students from diverse backgrounds, this introduction reviews recent developments in teacher education to situate our thematic issue on critical teacher education for equitable learning in multilingual classrooms. The five empirical papers and two commentaries included in this issue focus on the connection between language, power, and critical consciousness to address equity concerns in teacher education as it pertains to supporting multilingual learners, asking: how do teacher education programs prepare teachers to work with diverse students in schools where there exists a long-standing history of marginalization and discrimination based on racial, economic, social backgrounds? In this introductory paper, we discuss the contributions of the papers included in the issue and share a vision for new ways for reimagining teacher education for multilingual 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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".