Multicultural citizenship for the highly skilled? Naturalization, human capital, and the boundaries of belonging in Canada’s middle-class nation-building
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it