Constructing ‘Canadians’: a discourse analysis of political constructions in the Discover Canada Citizenship Guide
Bibliographic record
Abstract
Under Stephen Harper’s leadership, the Conservative government took active steps to maintain a particular Canadian identity. This notion is grounded in changes made to immigration policies and in Discover Canada, “where nation-specific definitions of citizenship” (Winter, 2014, p. 1) are outlined extensively. Discover Canada provides immigrants access to a specific construction of national history, while also highlighting Canada’s pride in its multiculturalism. Thus, when considering Discover Canada as a representation of Canada’s national identity, this MRP will interrogate the underlying discourses on which it is based by critically examining the guidebook. While the guidebook attempts to be inclusive—by including sections on ‘Aboriginal Peoples’ and ‘Diversity in Canada’— it paradoxically provides a romanticized vision of history that fails to recognize persisting social inequalities resulting from a deeply rooted history of colonialism and systemic racism. This problematic portrayal of Canadian history, identity and multiculturalism may significantly disservice immigrants who seek meaningful inclusion and representation in Canada. Key words: Political constructions and representations; national identity; citizenship; racialized immigrants and minorities; multiculturalism.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.036 | 0.030 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".