Social Interactions and Legislative Activity
Bibliographic record
Abstract
Abstract We develop a model of social interactions, as well as strategic interactions that depend on such social activity, and use it to measure social complementarities in the legislative process. Our model allows for partisan bias and homophily in the formation of relationships, which then impact legislative output. We use it to show how increased electoral competition can induce increased social behavior and the nonlinear effects of political polarization on legislative activity. We identify and structurally estimate our model using data on social and legislative efforts of members of each of the 105th–110th U.S. Congresses (1997–2009). We find large spillover effects in the form of complementarities between the efforts of politicians, both within and across parties. Although partisanship and preference differences between parties are significant drivers of socializing, our empirical evidence paints a less polarized picture of the informal connections of legislators than typically emerges from legislative votes alone.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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 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".