Toward an antiracist genre pedagogy: Considerations for a North American context
Bibliographic record
Abstract
Abstract This article combines principles from critical race theory and genre pedagogy to show how K–12 English language teachers can engage in antiracist genre‐based literacy instruction. Genre pedagogy has become increasingly popular in North America as an approach to supporting multilingual students’ literacy development. However, genres of schooling are often treated as ideologically neutral and not related to the structural racism that permeates North American schools. As a result, genre pedagogy is implemented in ways that reproduce dominant practices and reinforce deficit perspectives of multilingual students of color. Therefore, we propose principles for an antiracist genre pedagogy that (1) teaches community countertexts alongside dominant ones, (2) identifies ideologies and knowledge structures in each, (3) increases focus on interpersonal meanings to analyze racializing dimensions of texts, (4) promotes remixing genres for antiracist purposes, and (5) destabilizes the white measuring stick used to evaluate “appropriate” classroom language use. By combining functional methods with radical goals, we take seriously Ladson‐Billings’s (2014) call for remixing pedagogies in ways that benefit and empower students who have been disserved by dominant schooling ideologies—in this article, multilingual students of color.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| 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 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".