Destandardizing English: Seeking Linguistic Justice in the English Language Arts Classroom
Bibliographic record
Abstract
Black students and families in the United States have been consistently underserved by educational institutions and their curriculum.Scholars are increasingly aware of the opportunity gaps that arise as a result of Eurocentric standardized pedagogy and curriculum, and educators have turned to scholarship such as critical race theory to combat racial inequity; however, current legislation threatens such antiracist tools.As the National Council of Teachers of English reports in "Educators' Right and Responsibilities to Engage in Antiracist Teaching", over half of the country is burdened with "legislation either passed, pending, or under discussion [that] would severely limit K-12 and university educators' ability to engage with critical race theory and antiracist teaching" ( 2021).What do these restrictions mean to the educator who wishes to validate, discuss, and foster the experiences of Black students?How can we, as engaged teachers, practice and foster cultural literacy skills that encourage students to find appreciation for a diverse world?How does one ensure Black students see themselves in what is being taught?My research investigates all of these questions through the lenses of English Education and linguistic justice concluding that antiracist teaching in the ELA classroom remains possible and crucial, even at a time where legislation challenges it.I explore the origins of and literature that uses Ebonics as a way to help educators make learning more representative and equitable.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.018 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".