Dual citizenship in global perspective : from unitary to multiple citizenship
Bibliographic record
Abstract
Introduction: The Shifting Boundaries of the Political T.Faist PART ONE: STATES AND HUMAN SECURITY Dual Citizenship and Security Norms in Historical Perspective P.Triadafilopoulos The Securitization of Dual Citizenship A.Macklin PART TWO: NATIONALISM AND NATIONHOOD The Trade-off between Transnational Citizenship and Political Autonomy R.Bauboeck The Politics of Dual Citizenship in Hungary M.M.Kovacs Migration and Transnational Citizenship in Latin America: The Cases of Mexico and the Dominican Republic J.Itzigsohn Varying Views in Democracy, Rights and Duties, and Membership: The Politics of Dual Citizenship in European Immigration States J.Gerdes & T.Faist PART THREE: POSTNATIONAL AND TRANSNATIONAL CITIZENSHIP Much Ado about Nothing? The Contours of Dual Citizenship in the United States and Canada I.Bloemraad Dual Citizenship: A Postnational View P.Spiro Dual Citizenship among Hong Kong-Canadians: Convenience or Commitment? V.Preston, M.Siemiatycki & A.Kobayashi PART FOUR: CITIZENSHIP AND DEMOCRACY BEYOND BORDERS Dual Citizenship, European Identity and Community-Building in Europe W.Skrobacki Twilight of Sovereignty or the Emergence of Cosmopolitan Norms? Rethinking Citizenship in Volatile Times S.Benhabib Conclusion: The Boundaries of Citizenship in a Transitional Age P.Kivisto
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".