Bibliographic record
Abstract
Abstract In this review article on race and language teaching, we highlight an urgent need for the international educational community to continue to develop a complex understanding of how language teaching and learners’ lives are shaped by our global history of racist practices of colonial expansion, including settler colonialism and transatlantic slavery. We outline the genesis of research on race and language teaching and review literature that reflects a recent increase in scope and range of studies that problematize the workings of race and racism in language teaching and point to hopeful solutions for addressing effects of racial inequities. We conceptualize two key terms, ‘race’ and ‘language,’ then overview theories that appeared most significant in the research literature. We explore five interconnected themes that featured prominently throughout the existing literature on race and language teaching: standard language ideology and racial hegemony, the idealized and racialized native speaker, racial hierarchies of languages and language speakers, racialization and teacher identity, and race-centered approaches to pedagogies and educational practices. We offer a critical analysis of the current status of scholarship on race and language teaching, including gaps and necessary reframing, and conclude with implications for future directions and questions arising from the work.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".