From Migrants to Citizens: Membership in a Changing World
Bibliographic record
Abstract
Citizenship policies are changing rapidly in the face of global migration trends and the inevitable ethnic and racial diversity that follows. The debates are fierce. What should the requirements of citizenship be? How can multi-ethnic states forge a collective identity around a common set of values, beliefs and practices? What are appropriate criteria for admission and rights and duties of citizens? This book includes nine case studies that investigate immigration and citizenship in Australia, the Baltic States, Canada, the European Union, Israel, Mexico, Russia, South Africa and the United States. This complete collection of essays scrutinizes the concrete rules and policies by which states administer citizenship, and highlights similarities and differences in their policies. From Migrants to Citizens , the only comprehensive guide to citizenship policies in these liberal-democratic and emerging states, will be an invaluable reference for scholars in law, political science, and citizenship theory. Policymakers and government officials involved in managing citizenship policy in the United States and abroad will find this an excellent, accessible overview of the critical dilemmas that multi-ethnic societies face as a result of migration and global interdependencies at the end of the twentieth century.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".