Inventing “the White Voice”: Racial Capitalism, Raciolinguistics & Culturally Sustaining Pedagogies
Bibliographic record
Abstract
Abstract In this essay, I explore how paradigms like raciolinguistics and culturally sustaining pedagogies can offer substantive breaks from mainstream thought and provide us with new, just, and equitable ways of living together in the world. I begin with a deep engagement with Boots Riley and his critically acclaimed, anticapitalist, absurdist comedy Sorry to Bother You, in hopes of demonstrating how artists, activists, creatives, and scholars might: 1) cotheorize the complex relationships between language and racial capitalism and 2) think through the political, economic, and pedagogical implications of this new theorizing for Communities of Color. In our current sociopolitical situation, we need to continue making pedagogical moves toward freedom that center and sustain Communities of Color in the face of the myriad ways that white settler capitalist terror manifests. As we continue to theorize the relationships between language and racial capitalism, frameworks like raciolinguistics and culturally sustaining pedagogies provide fundamentally critical, antiracist, anticolonial approaches that reject the capitalist white settler gaze and its kindred cisheteropatriarchal, English-monolingual, ableist, classist, xenophobic, and other hegemonic gazes. What they offer us, instead, is a break from the assimilationist politics of the past and a move toward abolitionist frameworks of the future.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".