Still Making Canada White: Racial Governmentality and the “Good Immigrant” in Canadian Parliamentary Immigration Debates
Bibliographic record
Abstract
Reflecting on restrictive reforms to Canadian immigration laws in the 1990s, Sherene Razack has noted the emerging significance of racialized elites’ role in the policing of bodies of colour. Central to her analysis has been the national story of Canada as a peaceful and civilized “country of immigrants” that values cultural diversity and whose generosity is periodically besieged by masses of foreign criminals. This article, which was written two decades after Razack’s study, analyzes the racialized discourse of Canadian parliamentary debates on immigration and, in particular, the role that Conservative Asian members of parliament have played in debates throughout the consideration of Bill C-11, An Act to Amend the Immigration and Refugee Protection Act, which became the Immigration and Refugee Protection Act, and Bill C-31, Protecting Canada’s Immigration System Act, which became law in 2012. The article examines how Conservative Asian political elites have been drawn into hegemonic national stories in parliamentary debate on immigration by rehearsing “good immigrant” stories that distinguish “legitimate” from “illegitimate” immigrants.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.100 | 0.050 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| 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".