All in the Family: Partisan Disagreement and Electoral Mobilization in Intimate Networks—A Spillover Experiment
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We advance the debate about the impact of political disagreement in social networks on electoral participation by addressing issues of causal inference common in network studies, focusing on voters' most important context of interpersonal influence: the household. We leverage a randomly assigned spillover experiment conducted in the United Kingdom, combined with a detailed database of pretreatment party preferences and public turnout records, to identify social influence within heterogeneous and homogeneous partisan households. Our results show that intrahousehold mobilization effects are larger as a result of campaign contact in heterogeneous than in homogeneous partisan households, and larger still when the partisan intensity of the message is exogenously increased, suggesting discussion rather than behavioral contagion as a mechanism. Our results qualify findings from influential observational studies and suggest that within intimate social networks, negative correlations between political heterogeneity and electoral participation are unlikely to result from political disagreement.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it