The North American Naturalization Gap: An Institutional Approach to Citizenship Acquisition in the United States and Canada
Bibliographic record
Abstract
Using 1990 U.S. Census 5% PUMS and 1991 Canadian Census 3% public and 20% restricted microfiles, this article demonstrates the existence of a North American naturalization gap: immigrants living in Canada are on average much more likely to be citizens than their counterparts in the United States, and they acquire citizenship much faster than those living south of the border. Current theories explaining naturalization differences - focusing on citizenship laws, group traits or the characteristics of individual migrants - fail to explain the naturalization gap. Instead, I propose an institutional approach to citizenship acquisition. States' normative stances regarding immigrant integration (interventionist or autonomous) generate integrated or disconnected institutional configurations between government, ethnic organizations and individuals. Evidence from a case study of Portuguese immigrants living in Massachusetts and Ontario suggests that in Toronto government bureaucrats and federal policy encourage citizenship through symbolic support and instrumental aid to ethnic organizations and community leaders. In contrast, Boston area grassroots groups are expected to mobilize and aid their constituents without direct state support, resulting in lower citizenship levels.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".