Confronting Epistemological Racism, Decolonizing Scholarly Knowledge: Race and Gender in Applied Linguistics
Bibliographic record
Abstract
Abstract Recent scholarship in sociolinguistics and language education has examined how race and language intersect each other and how racism influences linguistic and educational practices. While racism is often conceptualized in terms of individual and institutional injustices, a critical examination of another form of racism—epistemological racism—problematizes how racial inequalities influence our knowledge production and consumption in academe. Highlighting the importance of the intersectional nature of identity categories, this conceptual article aims to draw scholars’ attention on how epistemological racism marginalizes and erases the knowledge produced by scholars in the Global South, women scholars of color, and other minoritized groups. In today’s neoliberal culture of competition, scholars of color are compelled to become complicit with white Euro-American hegemonic knowledge, further perpetuating the hegemony of white knowledge while marginalizing women scholars of color. Valorizing non-European knowledge and collectivity as an alternative framework also risks essentialism and male hegemony. Conversely, the ethics promoted by black feminism emphasizes a personal ethical commitment to antiracism. Epistemological antiracism invites scholars to validate alternative theories, rethink our citation practices, and develop critical reflexivity and accountability.
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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 | 0.049 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.018 | 0.080 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".