Citizenship Today: Global Perspectives and Practices
Bibliographic record
Abstract
The forms, policies, and practices of citizenship are changing rapidly around the globe, and the meaning of these changes is the subject of deep dispute. Citizenship Today brings together leading experts in their field to define the core issues at stake in the citizenship debates. The first section investigates central trends in national citizenship policy that govern access to citizenship, the rights of aliens, and plural nationality. The following section explores how forms of citizenship and their practice are, can, and should be located within broader institutional structures. The third section examines different conceptions of citizenship as developed in the official policies of governments, the scholarly literature, and the practice of immigrants and the final part looks at the future for citizenship policy. Contributors include Rainer Baubock (Austrian Academy of Sciences), Linda Bosniak (Rutgers University School of Law, Camden), Francis Mading Deng (Brookings Institute), Adrian Favell (University of Sussex, UK), Richard Thompson Ford (Stanford University), Vicki C. Jackson (Georgetown University Law Center), Paul Johnston (Citizenship Project), Christian Joppke (European University Institute, Florence), Karen Knop (University of Toronto), Micheline Labelle (Universite du Quebec a Montreal), Daniel Salee (Concordia University, Montreal), and Patrick Weil (University of Paris 1, Sorbonne)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.046 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".