Political Parties as Drivers of U.S. Polarization: 1927-2018
Bibliographic record
Abstract
The current polarization of elites in the U.S., particularly in Congress, is frequently ascribed to the emergence of cohorts of ideologically extreme legislators replacing moderate ones.Politicians, however, do not operate as isolated agents, driven solely by their preferences.They act within organized parties, whose leaders exert control over the rank-and-file, directing support for and against policies.This paper shows that the omission of party discipline as a driver of political polarization is consequential for our understanding of this phenomenon.We present a multi-dimensional voting model and identification strategy designed to decouple the ideological preferences of lawmakers from the control exerted by their party leadership.Applying this structural framework to the U.S. Congress between 1927-2018, we find that the influence of leaders over their rank-and-file has been a growing driver of polarization in voting, particularly since the 1970s.In 2018, party discipline accounts for around 65% of the polarization in roll call voting.Our findings qualify the interpretation of -and in two important cases subvert -a number of empirical claims in the literature that measures polarization with models that lack a formal role for parties.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".