“Would you take a pay cut? We're looking for Caucasians”. How whiteness can affect White, Black and Muslim female ‘native-speaker’ English language teachers
Bibliographic record
Abstract
This article sets out investigate the interrelationships between native speakerism, Whiteness, ethnicity and appearance in the TESOL context. It explores whether Whiteness plays a part in TESOL teachers' recruitment and job opportunities when employers are seeking to employ 'native speakers'. It draws its data from focus group interview data with seven female TESOL teachers. Two were White, three were Black and two were White Muslims who wear the hijab. The findings show that when English language teaching job advertisements call for 'native speaker' teachers, recruiters are-consciously or unconsciously-looking for White teachers from ex-colonising countries such as the USA, the UK, Canada, Australia and New Zealand whom they perceive as representing Whiteness. In this sense, Whiteness is inextricably linked to the concepts of the 'native speaker' and 'native speakerism' in English language teaching. The study concludes that native speakerism acts a veiled façade for Whiteness and consequently that White TESOL native speaker teachers are privileged over their Black and Muslim counterparts in a number of areas. These include: pay, objectification, acknowledgement of their professional achievements and visibility in advertising materials aimed at prospective students and their parents. The paper concludes with a call to confront such often-unacknowledged bias in favour of Whiteness by establishing open conversations with recruiters, parents, students and others involved in the TESOL field. It also recommends that countries should follow the European Union's lead and ban any language teaching job criteria that state a 'native speaker' requirement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".