Black English and Mathematics Education: A Critical Look at Culturally Sustaining Pedagogy
Bibliographic record
Abstract
Background/Context: The popularity surrounding culturally sustaining pedagogy (CSP) is notable primarily within language and literacy content areas but is also making its rounds in other disciplines. Because of its assumed objectivity and status, the mathematics discipline has long been a site of disrupting or perpetuating inequity and thus warrants our focus in thinking about how any pedagogical framework influences the success of Black students. We question whether the ideology undergirding CSP is beneficial to the ways in which we seek to educate Black mathematics learners through a philosophy of mathematics education that prioritizes language, and Black English more specifically. Purpose/Objective: The objective of this theoretical paper is to take a closer look at the shift in education research that has drawn attention away from asset-based frameworks, like culturally relevant pedagogy (CRP) or culturally responsive teaching, to focus on CSP. We seek to address how Black children may be positioned by the ideologies proposed in CSP, particularly in the way that culture, and Blackness in itself, is performed. Our goal is to highlight potential oversights in this framework and to discuss whether CSP represents a viable solution for Black students, particularly in regard to their mathematics education. Research Design: We elucidate the genesis of CSP and establish its roots as in tension with a Black ontology by tracing the theoretical origins of two scholars known best for its conceptualization. Findings/Results: Conceptualizers of CSP build on Ben Rampton’s work using the concept of “language crossing” and “styling the other,” to level critiques at CSP based on their studies of ritual insults in battle rap and use of African American Language in schools with multiethnic youth. Although it is true that people engage in language-crossing processes quite often, we question whether these actions of language crossing support claims about the eradication of cultural ownership, and at what cost Black people lose these aspects of culture. Conclusions/Recommendations: There are valuable lessons learned from Paris’s undertaking, but what Ladson-Billings provided in her theory of CRP was a more powerful conceptualization of improving education for Black students across multiple content areas because of the ways in which it forgoes the often conservative stance on language proffered in the theoretical ideologies of CSP. Discourse and language are inherently significant aspects of mathematics teaching and learning, and Black English is one possible entry into culturally relevant mathematics.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.031 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".