Racism and English Language Learning: Employing an Anti-Racist Approach to English as an Additional Language Education
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
Around the world, there is a growing number of linguistically diverse students enrolling in schools with English as the medium of instruction, and most educators are “interacting on a daily basis with learners with backgrounds and experiences different from his or her own” (Dei, 1996, p. 9). Guided in part by Srivastava’s (2007) pedagogical questions about “how we learn racist knowledge, how we perpetuate racist practices, and how we can change our everyday practices” (p. 306), this paper uses ideas from critical race theory (CRT), Dlamini’s (2002) interpretation of critical pedagogy, and Dei’s (1996) principles of anti-racism education to examine the intersection of racism and language, especially in relation to ELLs in K-12 schools in Canada. The author offers suggestions for EAL educators who are looking to implement anti-racist practices in the EAL classroom.
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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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