MétaCan
Menu
Back to cohort
Record W4416942093 · doi:10.1017/psrm.2025.10053

What can dual citizens teach us about political engagement?

2025· article· en· W4416942093 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePolitical Science Research and Methods · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsUniversité du Québec à Montréal
FundersUniversity of Cambridge
KeywordsPoliticsWitnessCitizenshipDual (grammatical number)Leverage (statistics)Political socializationDemocracyImmigration

Abstract

fetched live from OpenAlex

Abstract While we witness historic changes taking place in the conception and practice of citizenship, we know little about the political consequences it may bring. What are the effects of citizenship, as a status and a process, on political engagement? To gain leverage in addressing this question, we draw on citizenship categories that combine birthplace and the number of citizenship held. We compare US-born dual citizens to both naturalized-dual citizens and US-born mono citizens, which allows us to distinguish between the potential effects of socialization and the additional legal status. The study analyses two large nationally representative samples, presenting the first look at dual citizens in the United States. Results indicate that among dual citizens, those born in the US tend to participate more in politics than immigrants who naturalized. Among US-born citizens, the political participation of dual and mono citizens varies depending on the type of political activity. The study contributes to theoretical discussions on the relationship between an evolving citizenry and democratic participation.

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.

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)0.018
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.015
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.196
GPT teacher head0.597
Teacher spread0.400 · 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