Bibliographic record
Abstract
This article reconciles two seemingly incompatible expectations about interpersonal interaction and social influence. One theoretical perspective predicts that an increase in interaction between two actors will promote subsequent convergence in their attitudes and behaviors, whereas another view anticipates divergence. We examine the role of political identity in moderating the effects of interaction on influence. Our investigation takes place in the U.S. Senate—a setting in which actors forge political identities for public consumption based on the external constraints, normative obligations, and reputational concerns they face. We argue that interaction between senators who share the same political identity will promote convergence in their voting behavior, whereas interaction between actors with opposing political identities will lead to divergence. Moreover, we theorize that the consequences of political identity for interpersonal influence depend on the local interaction context. Political identity’s effects on influence will be greater in more divided Senate committees than in less divided ones. We find support for these hypotheses in analyses of data, spanning over three decades, on voting behavior, interaction, and political identity in the Senate. These findings contribute to research on social influence; elite integration and political polarization; and identity theory.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".