Who Claims Dual Citizenship? The Limits of Postnationalism, the Possibilities of Transnationalism, and the Persistence of Traditional Citizenship
Bibliographic record
Abstract
The dynamics of globalization, especially international migration, challenge traditional frameworks of citizenship and prompted scholars to develop new models of membership: transnationalism and postnationalism. All three – the traditional, transnational and postnational – explicitly or implicitly address the controversial topic of dual citizenship, or multiple membership. Lack of statistical data, however, has made it difficult to adjudicate between these models or to undertake a broad empirical assessment of dual citizenship, either over time or between people from different countries and socioeconomic backgrounds. This article outlines the testable implications of traditional, transnational and post-national frameworks and evaluates these hypotheses using a unique statistical data source that asked respondents to report multiple citizenship, the 1981, 1991 and 1996 20% Canadian census samples. The data offer little evidence that immigrants adopt a strict postnational view of citizenship, but they reveal the possibilities of transnationalism and the continued relevance of traditional frameworks. Over time, we observe a rapid increase in the aggregate level of reported dual citizenship from 1981 to 1996. We also find that those with higher human capital, rather than the economically marginalized, are more likely to embrace dual citizenship. After controlling for individual attributes, important contextual or group effects nonetheless remain: self-reports of dual citizenship vary significantly by birthplace and are higher if an immigrant lives in Quebec. Since naturalization levels seem to rise in tandem with reports of dual citizenship, this research suggests a certain paradox: while multiple belonging undermines some aspects of conventional state sovereignty, dual citizenship might be a means for countries to promote immigrants’ political and legal attachments.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".