Native Speakerism and Employment Discrimination in English Language Teaching
Bibliographic record
Abstract
The terms ‘native speaker’ and ‘non-native speaker’ are commonly used in English Language Teaching (ELT). Such terminologies create segregation and negatively affect the morale of some non-native English-speaking teachers (NNEST). Using the lens of Critical Race Theory, this paper investigates native speakerism (NS) through a review of literature, specifically on hiring practices or employment discrimination in ELT. It intends to contribute to the dismantling of such native and non-native speaker dichotomy and to establish a more impartial and equitable ELT profession. The terminologies in this literature review were selected through a keyword selection; namely, “native speakerism,” “employment discrimination,” and “hiring practices.” Google Scholar and the online library of a large research university were employed to search for publications in a comprehensive list of databases, including JSTOR, English Teaching & Learning, and ERIC. Using thematic content analysis, articles were categorized into the following themes: Native Speaker Preference, Hiring Criteria, Salary, Advertisements, and Microaggressions. The analysis of 14 relevant articles shows that employment discrimination still prevails in the ELT profession. As studies on employment discrimination in the Canadian ELT industry are lacking, there is a dire need to conduct further research in this area and context.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".