Neoliberalism, native-speakerism and the displacement of international students’ languages and cultures
Bibliographic record
Abstract
With the number of international students growing rapidly within (international) higher education, more attention has been focused on the need to consider international students’ experiences, particularly those from the global south, from more critical, ethical and qualitative perspectives. This paper examines how the lived experiences of three multilingual international students at a Canadian university were impacted by ideologies stemming from neoliberalism and native-speakerism within higher education. Through in-depth interviews with each student, the findings point to complex ways in which such ideologies gradually worked to displace the students’ languages and cultures through processes of othering and inferiorisation. More specifically, the combined sociocultural and material impact of neoliberalism and native-speakerism resulted in the students appearing to reject participation in and affiliation to their cultural groups, repositioning their languages as deterrents to the development of their English language proficiencies, and adopting behaviours that could linguistically and socially approximate them to an imagined native speaker of ‘standard’ English, including attending speech therapy. The conclusion critically discusses the importance of reform in higher education with respect to language, diversity and social justice.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.037 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".