iTutorGroup: A case study of covert native-speakerism underneath a social justice façade
Bibliographic record
Abstract
This article examines the covert native-speakerist strategies iTutorGroup utilizes to discriminate against teachers of nationalities the company appears to deem as undesired. Through content analysis of numerous job application submissions to iTutorGroup’s website, results show iTutorGroup’s automatic hiring process offers teachers of these nationalities a much lower potential wage and only a video-recorded asynchronous interview, if not complete refusal to an interview. In contrast, British, Australasian, and North American nationals are afforded a much higher potential wage as well as a one-on-one live interview. The company conceals these nuanced discriminatory strategies with a façade of equality since they are one of TESOL International Association’s Global Partners. As a Global Partner, iTutorGroup follows suit in pretending to uphold TESOL’s nondiscrimination policies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".