“I Didn't Get Jobs Because of the Way I Spoke, and Because of Where I Came from”: How English ‘Native Speaker’ Teachers’ Accents Affect Their Employment Opportunities
Bibliographic record
Abstract
This article highlights the interrelationship between native-speakerism and langlism, where both concepts entail discriminatory recruitment practices against a large cohort of teachers. Given its greater concern with the accents of English teachers than with their status as ‘native’ speakers of English and their teaching competence, langlism negatively affects many English language teachers because of their ‘non-standard accents.’ In order to tackle the issue in more depth, a focus-group interview was conducted with seven English language teachers who were hired on the basis that they were ‘native speakers.’ The findings show that langlism affects not only ‘non-native speaker’ teachers of English but also pertains to ‘native speaker’ teachers who do not have the accent required by recruiters. The study concludes that native-speakerism has more discriminatory implications and that these go beyond the conventional ‘native’ versus ‘non-native’ speaker dichotomy by reaching into a teacher’s accent, regardless of his or her ‘native’ status.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".