Critical Race Theory and Intersectionality: Race, Culture, and Identity in the ESL Classroom
Bibliographic record
Abstract
This paper discusses the use of Critical Race Theory (CRT), intersectionality, and different teaching approaches in K-12 ESL classrooms to introduce race-related concepts, culture, and identity. It highlights the importance of such theories and concepts in achieving inclusivity and creating a welcoming learning environment, underlining the need for research on developing in-class activities that focus on culture, identity, and race. The paper begins with an overview of CRT and intersectionality, emphasizing their pertinence in TESOL and ESL pedagogy. Then, different teaching methods such as Culturally Responsive Teaching and Social Justice Education are discussed highlighting their benefits and how they can be used to introduce race, culture, and power relations to English Language Learners (ELLs). The paper concludes with a discussion of the role of literature, counter-stories, and critical literacy in teaching ELLs about race, culture, and identity, accompanied by practical in-class activity suggestions. This paper not only introduces educators to CRT and intersectionality but also provides a range of practical activities and insights for effectively incorporating race-related topics in ESL education.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".