Knowledge economy discourses and language regulation : an analysis of policy processes in adult English language education in Canada
Bibliographic record
Abstract
This thesis examines the policy processes, social and power relations, and textual practices that have come to regulate English language education and assessment of internationally educated professionals (IEPs) who immigrate to Canada. During migration and settlement processes, IEPs whose first language is not English undergo assessment of their English language abilities before they can begin practicing in their professional field. Within discourses of building a global knowledge economy, where knowledge workers move across national borders and are expected to demonstrate English language proficiency, variation in communicating in English is constructed as a problem. Different ways of speaking English can become a barrier to labour market integration for IEPs. The Canadian government’s policy response to this problem has been the development of a competency-based language framework to regulate and standardize the assessment of immigrants’ English language proficiency. This study analyzes the tensions and struggles involved in standardizing and regulating the English language and its assessment in relation to knowledge economy discourses. Employing Dorothy E. Smith’s (2005) institutional ethnography, key reports and policy documents (e.g. The Canadian Language Benchmarks) were analyzed. The study then explored the experiences of language experts (the researchers/consultants, teachers, and provincial government administrators) who are responsible for profiling the English language demands of various professions (e.g. nursing, engineering, accounting) for the development and implementation of profession-specific language exams and programs. The study documented competing interests and conflicting worldviews on the purposes and processes of English language assessment. It also analyzed how textual practices were informed by knowledge economy discourses. The findings contribute to existing research on foreign credential recognition by demonstrating how the power relations and dominant discourses embedded in policy processes of language assessment significantly contribute to the (non)-recognition of IEPs’ professional knowledge and skills. The study also contributes to the limited research on the CLB by focusing on the labour market integration of IEPs in relation to knowledge economy discourses. Finally, the findings contribute to theoretical and empirical research in which language is understood as a social practice, drawing attention to the problematic of standardizing language and assessment practices in work and professional contexts.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.038 | 0.022 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".