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Record W7045559454

Becoming and Belonging?: Lived experiences of naturalization and the implementation of citizenship law in Germany and Canada

2025· article· en· W7045559454 on OpenAlexaboutno aff

Bibliographic record

VenueLeiden Repository (Leiden University) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipNaturalizationDiscretionIdentity (music)PoliticsPerceptionActive citizenshipImmigration
DOInot available

Abstract

fetched live from OpenAlex

<table><tbody><tr><td> 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.<br>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. </td></tr></tbody></table>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.245
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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