Imagining Canada, imagining Canadians: National identity in English as a second language textbooks
Bibliographic record
Abstract
In this study, I establish that language textbooks are sites of discursive struggle through which nationalist imagined communities are reproduced. I use critical discourse analysis to analyze how these textbooks construct Canadian identities that position students in relation to an imagined community of Canada. I analyze twenty-four textbooks and three Citizenship and Immigration Canada publications used in government-funded language instruction in Ontario. Representations of Canada and Canadianness in the texts examined include and exclude student readers, participate in banal nationalism, and legitimate particular understandings of Canada. The identified textbooks mark nationality through flags, maps, references to nation, and the use of nation as a frame of reference. The textbooks also make claims about how 'Canadians' think and behave. This banal nationalism naturalizes and essentializes imaginings of 'Canada' and 'Canadianness' supporting particular and interested constructions and positive evaluations of 'Canadian' identity. Both government produced publications and identified textbooks legitimate constructions of Canadian identity through repeated positive representations of Canadianness; the marginalising inclusions of 'others'; the subordination of gendered, racialised, and classed social positions to nation; and by maintaining a low level of dialogicality overall.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".