The Persistence of Participation: An Analysis of American Expatriate Involvement in US Campaigns
Bibliographic record
Abstract
In this project, I will draw from four survey datasets: two original online surveys of Americans living in Canada, fielded during the 2020 and 2022 US campaigns (Ronald Rapoport is co-PI); and the 2020 / 2022 ANES cross-sectional surveys. The focus of the study is on modeling campaign involvement. The literature on campaign involvement in the US has long recognized three major types of factors that shape participation: attitudes (especially interest in politics, efficacy, strength of partisan identification, strength of ideological preferences, and differential assessments of the major political figures in the campaign); political resources and demographic background traits (especially education level, gender, age, race/ethnicity); and mobilization (especially contacts with candidate and party organizations). These are the core pillars of classic participation models (e.g., Verba et al, Rosenstone et al., Campbell et al.). In this investigation, I will fit comparable regression models within the four datasets. To what extent are the findings from the Canada-based American emigrant samples comparable to those from the ANES? My expectation is that the factors underlying involvement are sufficiently persistent that they will replicate across borders. I.e., the same factors that condition campaign involvement in the US case will be evident in the Canadian samples, to approximately the same degree. Substantively, this would underscore the significance of long-term socialization forces and habits (H1). Alternatively, when documenting hypotheses below, I also allow for expatriate transnational participation to depend more heavily on attitudes, resources, demographic background traits, and contacts with mobilizers given the lower prominence and salience of American electoral politics in their country of residence (H1a). In follow up models, I will then add to the Canadian models a number of migration-related predictors to examine how incorporation into another society and political system shapes transnational participation. These predictors are: time spent in Canada; whether or not the respondent is planning to return to the US; degree of identification as "Canadian"; and involvement in a Canadian political party. Based on prior research from the literature on transnational migration, I expect that these migration-related variables will have only modest effects on US campaign involvement; i.e., there is no major tension between social and political incorporation as an "immigrant" and involvement as an "emigrant." (H2). Alternatively, I also leave open the possibility that migration-related variables may condition the effects of attitudes, resources, demographic profile, and mobilizing contacts such that these predictors will matter more for emigrants who are less incorporated into the receiving country. (H3)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.024 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".