Exploring Canadian Integration through Critical Discourse Analysis of English Language Lesson Plans for Immigrant Learners
Bibliographic record
Abstract
The Canadian government implemented the Language Instruction for Newcomers to Canada (LINC) program to help immigrants integrate into Canada. However, research indicates that the LINC program fails to achieve its integrative goals. Using Fairclough’s analytical concepts of genre, discourse and style, this article closely examines a unit of LINC lesson plans to understand how they advance an understanding integration among classroom stakeholders (Canadian teachers and immigrant students). The analysis reveals that the LINC curriculum proliferates inequality between Canadians and newcomers by fostering an assimilative orientation that subjugates immigrants as problematic Others. Immigrants are expected to conform to dominant Canadian ways of being and ways using language. This article calls for a rethinking of integration by identifying possibilities for resistance that could shift immigrants’ positioning and reduce discrimination. This could occur through better recognizing bias and dominance, and through acknowledging and validating newcomers’ ways of speaking and interacting.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".