Bibliographic record
Abstract
Almost all Western countries have recently implemented restrictive changes to their citizenship law and engaged in heated debates about what it takes to become “one of us”. This article examines the naturalization process in Canada, a country that derives almost two thirds of its population growth from immigration, and where citizenship uptake is currently in decline. Drawing on interviews with recently naturalized Canadians, I argue that the current naturalization regime fails to deliver on the promise to put “Canadians by choice” at par with “Canadians by birth”. Specifically, the naturalization process constructs social and cultural boundaries at two levels: the new citizens interviewed for this study felt that the naturalization process differentiated them along the lines of class and education more than it discriminated on ethnocultural or racial grounds. A first boundary is thus created between those who have the skills to easily “pass the test” and those who do not. This finding speaks to the strength and appeal of Canada’s multicultural middle-class nation-building project. Nevertheless, the interviewees also highlighted that the naturalization process artificially constructed (some) immigrants as culturally different and inferior. A second boundary is thus constructed to differentiate between “real Canadians” and others. While not representative, the findings of this study suggest that the Canadian state produces differentiated citizenship at the very moment it aims to inculcate loyalty and belonging.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.017 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".