Raciolinguistic Disposability: The Experience of Filipino Teachers in China Before and During the COVID-19 Pandemic
Bibliographic record
Abstract
In July 2021, the Chinese government suddenly issued new rules to restrict private tutoring services and limit foreign investments in privately owned learning centres. This hasty policy rehaul stunned the growing community of Filipinos in the English Language Training (ELT) industry in China, as it undermined the landmark 2018 China–Philippines diplomatic agreement concerning the recruitment of Filipino teachers to China. My research on Filipino teachers in China in the pre-COVID-19 era highlights the uneven power dynamic in Asia and the global hierarchies in the education and language industries that shape and undo labour markets. My data exposes the precarity and disposability that labour migrants in the ELT industry face within conditions shaped by global hegemonies that are racial and linguistic. The experience of displacement of Filipino teachers in the ELT industry in China leads me to argue that this industry produces a condition of ‘raciolinguistic disposability’ affecting Filipino English teachers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| 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".