Examining Embedded Meaning in Canada’s English-Language Proficiency Requirements for Immigration, Asylum and Resettlement, and Citizenship
Bibliographic record
Abstract
Globally the number of people on the move is increasing as is the use of language proficiency testing for facilitating or blocking immigration, asylum and resettlement, and citizenship. This multi-manuscript dissertation examines the meaning that is embedded in Canada’s English-language proficiency requirements in these three instances where international migrants need to demonstrate a specific level of language proficiency. Despite Canada’s reputation as a global leader in immigration, this dissertation identifies several issues happening in the Canadian context that need more research and advocacy. The findings also revealed the challenges and complexities that even highly proficient and educated test takers face in meeting Canada’s requirements. The first manuscript describes current use of English-language proficiency requirements in top migrant destination countries globally, including Canada, and the factors that have led to their use. It also views different frameworks and definitions from the field of language testing through the lens of Shohamy’s (2001) Critical Language Testing and identifies a gap between the theoretical and conceptual work and empirical work in the migration context. The second manuscript reviews the only test designed and developed for immigration in Canada’s migration context, the Canadian English Language Proficiency Index Program (CELPIP) Test, for fit of purpose. Research on this de facto policy tool also highlights the same issues raised in the first manuscript. The third manuscript used interviews to explore test takers’ experiences in trying to meet Canada’s English-language proficiency requirements for permanent residency. This dissertation concludes by identifying the issues that thread through all three manuscripts which call into question Canada’s reputation as a global leader in immigration and by making recommendations for language testing professionals to answer the many calls from the field to engage with language test use within the global migration context.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".