Monolingualism, neoliberalism and language-as-problem: discourse itineraries in Canadian university language policy
Bibliographic record
Abstract
Internationalization policies to promote international student enrolment at many Canadian universities have led to increased levels of linguistic diversity in the student body. However, institutional language policy responses to this diversity may be lacking, may centre a monolingual mindset or may discursively position the issue of the English language proficiency and development of students from non-English-speaking backgrounds in a framing of deficit. This study maps changing and conflicting “discourse itineraries” (Scollon, 2008:234), the taken-for-granted ideas, constraints and allowances at play discursively in institutional language policy. This was done via a multiple case study of three Canadian universities, where critical discourse analysis (CDA) was carried out on a variety of policy documents related to language, academic literacy and internationalization at the provincial (macro) and institutional and faculty (meso) levels and stakeholders at these institutions were interviewed. This analysis revealed, first, that much language policy at these three institutions is covert, implicit and de facto. Two prominent discourses were also found: Language-as-Problem (Ruiz, 1984) and Neoliberalism and Language, each with pervasive sub-discourses—notably the Monolingual Mindset—that shape the creation of language policy at these universities. Discursive change is underway, however, as conflicting discourses were found at all institutions. In certain cases, there is a shift away from Language-as-Problem, influenced by a neoliberal focus on the English language as economic instrument. Building on Ruiz’s (1984) orientations toward language planning, this thesis proposes a new policy analytic heuristic to further describe the extent to which institutions ignore, blame, support or embrace language at different policy levels. As well, suggestions are made for Canadian higher education (HE) language stakeholders about how to realign discourses and bring about social change via critical language awareness-raising and policy-making. The ultimate goal is to provide a more equitable academic experience within Canadian HE for students from non-English-speaking backgrounds.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.065 | 0.081 |
| Scholarly communication | 0.032 | 0.010 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".