“Your English is so good”: Linguistic experiences of racialized students and instructors of a Canadian university
Bibliographic record
Abstract
Racism has increasingly been exposed and problematized in public domains, including institutions of higher education. In academia, critical race theory (CRT) has guided scholars to uncover everyday experiences of racism by highlighting the intersectionality of race with other identity categories, among which language constitutes an important, yet underexplored, component. Through the conceptual lens of CRT and counter-storytelling as a methodological orientation, this study investigated how racialized graduate students and faculty members at a Canadian university experienced racialization and racism in relation to issues of language, including communication and the use of ethnic names as semiotic markers. Individual and focus group interviews generated participants’ stories, to which we applied a thematic analysis. Participants generally felt that they were forced into pre-determined and essentialized categories of race, ethnicity, nationality, and language. Racialized non-native speakers of an official language—English or French—often received compliments or inquisitive comments on their language proficiency, which further accentuated their raciolinguistic Otherness and caused pain. Conversely, racialized native speakers did not report receiving compliments on language. For East Asian participants especially, speaking White English seemed to offset their racial stigma and psychologically separated them from non-native, English-speaking East Asian immigrants who looked like them. These experiences indicate normative expectations. The participants felt they were expected to not only speak, write, or communicate in the White normative language and manners, but also to use or not use an Anglicized name against their will. These impositions were questioned and resisted by some participants, and antiracist consciousness was expressed. The participants’ voices encourage universities to validate their stories as well as their ways of telling their stories.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.051 | 0.019 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 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".