Multicultural citizenship for the highly skilled? Naturalization, human capital, and the boundaries of belonging in Canada’s middle-class nation-building
Bibliographic record
Abstract
Taking Canada as a widely envied and imitated example of liberal, “difference-blind” economic immigration, in this paper, I examine the permeability, constraints, and symbolic meaning of the different requirements of the naturalization process from the perspective of those who have undergone the process. Based on interviews with recently naturalized Canadians, my study reveals that the three steps of the application process – filing the application, studying the citizenship guide and sitting the test, attending the citizenship ceremony and swearing the citizenship oath – constitute mostly blurred boundaries for skilled and highly educated immigrants, with occasional bright boundaries related to management flaws, classed naturalization, and cultural biases. Specifically, immigrants endowed with valued forms of human capital are naturalizing fast and easily even if they are members of racial, ethnic or religious minorities. This underscores the strength of multiculturalism as national identity and ethos of societal integration. However, the attainment of citizenship in the multicultural nation does not come quasi-automatically as a right for everyone after years of lawful residency. Rather, it is granted as an earned privilege only to those who demonstrate the successful mastery of the skills and mindset of middle-class professionals. Since naturalization now operates along the same econocentric logic that governs immigrant selection through the points system, individuals admitted through non-economic streams, such as refugees and immigrants in the family class are increasingly struggling with the naturalization process. This raises questions about the implicit biases and new fault lines of seemingly difference-blind middle-class nation-building through immigration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.019 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".