Discourses of Multicultural Acknowledgment in Canada: Liberal and Conservative, General and Specific
Bibliographic record
Abstract
Contemporary discourses of diversity and multiculturalism are contentious spaces in Western democracies. Symbolically, they have come to serve as a forum for governments to articulate their larger nation-building, citizenship and political agendas. In this sense, multiculturalism, whatever else it is, is a model of political belonging: a form of political symbolism directed at minorities and majorities to establish the conditions of belonging to the political community. This article examines the model(s) of belonging to political community in the Harper Conservative (2004–2015) and Trudeau Liberal (2015–2019) governments, presenting textual data derived from a broad discourse analysis. It illustrates that both use multiculturalism as a form of minority and majority acknowledgment that highlights the terms of belonging to the Canadian multicultural political community. Further, it reveals a difference in rhetorical strategy. while Conservative multiculturalism prioritizes a practice of specific minority acknowledgment that casts integration to the existing national identity as the responsibility of ethno-cultural communities, Liberal multiculturalism prioritizes the general acknowledgment of difference in order to broaden the national identity, and turn it outward to global relevance. Importantly, this illustrates the surprising variability and tenacity of multicultural symbolism in Canada.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.036 | 0.019 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".