A Citizen Just Like You: The Role of Complex Contagion and Resemblance for Decisions to Naturalize
Bibliographic record
Abstract
As is the case with the adoption of many other practices, social influence plays an important role in immigrants' decision to apply for host-country citizenship. Existing work uses residential characteristics to proxy social network effects but does not directly analyze the hypothesized mechanisms - flow of information and signals about identity fit - nor does it specify the type of social network influence. We address this lacuna using data from in-depth interviews with immigrants about their decision whether or not to naturalize in the US. Our analysis of this data suggests that for migrants facing barriers naturalization diffuses through complex contagion. Rather than through the simple presence of naturalized co-ethnics, we show that social influence flows through strong ties to naturalized immigrants who share similar characteristics. When assessing information about the process and their chances of success, those who are on the fence look to others who have naturalized and who resemble them in attributes like education or migration trajectory. In addition, the promise of status equality that citizenship offers matters: comparing themselves to (social) peers that have citizenship can motivate respondents to naturalize as a way to claim equal status.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".