"Interaction, Ideology, and Identity in the U.S. Senate, 1979-2001"
Bibliographic record
Abstract
This article reconciles two seemingly incompatible expectations about social interaction and ideological change. One theoretical perspective predicts that an increase in interaction between two actors will promote subsequent convergence in their ideologies, while another anticipates ideological divergence. Integrating network-analytic approaches to social influence with social psychological theories of identification, we argue that interaction between actors who share a salient social identity promotes ideological convergence, while interaction between actors with contrasting social identities leads to divergence. Moreover, the consequences of social identity for influence depend on the local context of interaction. Social identity’s effects on influence are greatest in groups with a limited, rather than extensive, history of prior collaboration and with moderate, rather than low or high, levels of ideological diversity. Empirical support for these propositions comes from analyses of the U.S. Senate from 1979 to 2001. Using two distinct indicators of social identity —party affiliation and region of representation—we demonstrate that, as the level of interaction between senators changed, the ideological distance between them subsequently shifted as a function of their respective social identities and characteristics of committees they served on together. These findings contribute to research on social influence, elite integration and political polarization, and social identity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".