Jumping Through Hoops in the Canadian Immigration System: A Critical View of the Immigrant's Journey to Citizenship
Bibliographic record
Abstract
Abstract Language assessment for citizenship is a ubiquitous enforced and enacted policy in several developed countries (e.g., Canada, the United States, the United Kingdom, Australia, and the Netherlands, to name a few). In this regard, language testers have expressly argued that this practice enacts injustice for and adds hurdles to marginalized immigrant groups (McNamara & Shohamy, 2009; Shohamy, 2009). Nevertheless, language proficiency tests remain a critical, high‐stakes criterion in evaluating immigrants' permanent residence and naturalization applications. To this end, language tests used for immigration and citizenship purposes are often aligned with widely recognized language frameworks such as the Canadian Language Benchmarks (Chen & Flasko, 2020) and the Common European Framework of Reference (Lim, Geranpayeh, Khalifa, & Buckendahl, 2013). In turn, these alignment studies idealize, in subtle ways, new Canadians with language proficiency requirements that would make them worthy of permanent residence or citizenship. Knowledge of society tests plays an essential role in immigrants' journey to citizenship and can also be considered tests of reading proficiency. This study focuses on the enacted Canadian language policy for prospective immigrants and citizens, adopting a corpus‐assisted discourse analytic approach (Taylor & Marchi, 2018) to the study guide for the Canadian citizenship test ( Discover Canada: The rights and responsibilities of citizenship ) . A primary focus is examining how official and nonofficial languages are represented within this document. The findings highlight some of the problematic assumptions that underpin the use of monolingual constructs in tests encountered in the journey to permanent residence and Canadian citizenship.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.102 | 0.056 |
| Scholarly communication | 0.022 | 0.007 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".