Becoming and Belonging?: Lived experiences of naturalization and the implementation of citizenship law in Germany and Canada
Bibliographic record
Abstract
Naturalization, the acquisition of citizenship, constitutes the final step of a migrant’s formal integration process. For many, becoming a citizen means gaining civic rights, protection from deportation, and freedom of movement. Citizenship policies are often interpreted as a reflection of a country's identity – what it values in a citizen – and are commonly used as an indicator of a country's general approach to immigration. Naturalization literature has long sought to determine and evaluate the precise factors deciding whether someone will become a citizen. This dissertation examines the process of citizenship acquisition, its impact on the individual naturalizing and the experiences of those enforcing citizenship policy day-to-day. It asks how someone acquires citizenship formally, administratively, and emotionally and how that citizenship is interpreted. How is citizenship law put into action? Does becoming a citizen mean that someone feels like they belong to that country? Employing a bottom-up approach, this research studies the implementation of citizenship policy utilizing in-depth interviews with both new citizens and street-level bureaucrats in Germany and Canada. It combines theoretical and methodological approaches from public administration, political science and socio-legal studies incorporating questions of discretion and the perception thereof, definitions of citizenship, and legal consciousness.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.022 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".