Bibliographic record
Abstract
Purpose – Expertise in English language teaching (ELT) is determined by being a white native speaker of English. Therefore, ELT is a type of aesthetic labour because workers are expected to look and sound a particular way. As nonwhite teachers cannot perform this labour, they may experience employment discrimination in the form of racial microaggressions, which are everyday racial slights. The purpose of this paper is to investigate what types of microaggressions inform several nonwhite teachers that they cannot perform aesthetic labour in private language schools in Toronto, Canada. Design/methodology/approach – The paper utilizes a critical race methodology in which several nonwhite teachers told stories of racial microaggressions. Findings – The teachers were told that they lacked the right aesthetic through microaggressions involving employers being confused about their names, questioning their language backgrounds, and citing customer preferences. Research limitations/implications – Future research must find out whether nonwhite teachers experience discrimination throughout Canada. Other studies must investigate how intersecting identity markers affect teachers’ employment prospects. Practical implications – To prevent the discrimination of nonwhite teachers (in Canada), increased regulation is needed. The international ELT industry also needs to fight against the ideology that English is a white language. Originality/value – There is little literature that examines language/racial discrimination in the Canadian ELT industry and how this discrimination is articulated to teachers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".