Racist nativist microaggressions and the professional resistance of racialized English language teachers in Toronto
Bibliographic record
Abstract
English language teaching upholds racist nativist notions that competent teachers are white native speakers of English born in majority-white countries. These notions manifest when international students, expecting to be taught by these speakers, are skeptical about having a racialized instructor, who may be seen as non-native to English and the nation where it is natively spoken. Rather than overt, this skepticism may appear in the form of microaggressions. Informed by critical race and resistance theories, this article uses interviews with 10 racialized teachers in Toronto, Canada to detail the racist nativist microaggressions that they experience at work and their professional resistance strategies that combat these microaggressions. The findings describe the following microaggressions: interrogations of the teachers’ nativeness, insinuations of their foreignness to English, and behavioral indications that they are ‘invading’ the classroom. Their professional resistance either conformed to or sought to transform notions of the supremacy of white (Canadian) teachers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.018 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".