Replication Data for: What Can Dual Citizens Teach Us about Political Engagement?
Bibliographic record
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 analyzes 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.
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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 | 0.016 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.093 | 0.043 |
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".