Science education with multilingual learners: Equity as access and equity as transformation
Bibliographic record
Abstract
Abstract Research in science education with multilingual learners (MLs) has expanded rapidly. This rapid expansion can be situated within a larger dialogue about what it means to provide minoritized students with an equitable education. Whereas some conceptions of equity focus on ensuring all students have access to the knowledge, practices, and language normatively valued in K‐12 schools ( equity as access ), increasingly prominent conceptions focus on transforming those knowledge, practices, and language in ways that center minoritized students and their communities ( equity as transformation ). In this article, we argue that conceptions of equity provide a useful lens for understanding emerging research in science education with MLs and for charting a research agenda. We begin by tracing how conceptions of equity have evolved in parallel across STEM and multilingual education. Then, we provide an overview of recent developments from demographic, theoretical, and policy perspectives. In the context of these developments, we provide a conceptual synthesis of emerging research by our team of early‐career scholars in three areas: (a) learning, (b) assessment, and (c) teacher education. Within each area, we unpack the research efforts in terms of how they attend to equity as access while pushing toward equity as transformation. Finally, we propose a research agenda for science education with MLs that builds on and extends these efforts. We close by offering recommendations for making this research agenda coherent and impactful: (a) being explicit about our conceptions of equity, (b) paying attention to the interplay of structure and agency, and (c) promoting interdisciplinary collaboration.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".