Science education with multilingual learners: Equity as access and equity as transformation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it