An inconvenient truth: When ideologies of multilingualism lead to auto‐inflicted epistemic exclusion by multilingual students in higher education
Bibliographic record
Abstract
Abstract In this article, we juxtapose two international contexts of higher education to critically examine both the situated complexity of (restrictive) ideologies of multilingualism and the ways such ideologies inform multilingual students’ choices of language use that contribute to their own epistemic exclusion in Canada and Germany. A content analysis of data from interviews and written reflections on language choice illustrates that the ideologies of (1) devaluation of partial repertoires, (2) maximalist view of language competences, (3) neoliberal multilingualism, and (4) native‐speakerism‐in‐multilingualism are enacted and reproduced by students themselves in both contexts, leading to auto‐inflicted epistemic exclusion. The findings reveal not only the pervasiveness of monolingualism within multilingualism and higher education, but also the hierarchization of languages and their (imagined) speakers, from which we conclude that not all forms of individual multilingualism are valued, despite increasing celebration of diversity in global higher education.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.037 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.009 |
| 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".