Blame the Message Senders Not the Messenger: The Defence Case of the English “Native Speaker” Teacher
Bibliographic record
Abstract
This article investigates how “native speaker” teachers define who a “native speaker” is and how they view themselves in relation to the concept. It further explores how they feel about discriminatory practices in employability and the pay gap that are systemically carried out against their “nonnative speaker” counterparts by recruiters. Data were gathered from 10 English language teachers: five males and five females from the UK, Canada, Ireland, and South Africa, who were hired by a state university in Saudi Arabia on the basis that they are “native speakers.” The findings show that although the place of birth and the official status of English in a given country were the main defining criteria for hiring a “native speaker,” the interviewees did not view the concept of the “native speaker” in the same ways as their recruiters did, who they believed used those criteria in an overly simplistic and reductive way rooted in native-speakerism. The findings also show that the participants did not enjoy the unjustified privileges given to them by their recruiters at the expense of their “non-native speaker” colleagues. Instead, in some cases, they attempted to confront their recruiters over such discriminatory practices, and in some others, they attempted to bridge the gap and ease the tension between themselves and their “nonnative speaker” counterparts, although these efforts were hindered by the system’s unfair and unjust practices.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".