The Conditional Lawmaking Benefits of Party Faction Membership in Congress
Bibliographic record
Abstract
Does joining a party faction in Congress enhance or undermine a member’s lawmaking effectiveness? Prior research suggests that factions can help members electorally in signaling their distinct ideological positions to potential political supporters. By contrast, we examine the nine largest ideological caucuses over the past quarter century to test three hypotheses about the conditional lawmaking benefits of faction membership: (1) that benefits from faction membership are limited to those in the minority party; (2) that members of ideologically centrist factions gain the greatest benefits; and (3) that sizable factions exploit their pivotal positions to help their members achieve legislative victories. We find support for only the first of these three conjectures, consistent with the argument that factions offer valuable resources to those in the minority party and that majority-party leaders counter the proposals arising from their own party’s factions. The fact that faction membership offers no significant lawmaking benefit to majority-party legislators challenges conventional wisdom.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".