Bibliographic record
Abstract
While legislative polarization at the national level has been carefully examined and largely explained, the causes of polarization in state legislatures have been more elusive. Studies examining factors such as gerrymandering and party primaries as possible explanations have found largely undetectable or at best very modest relationships between these variables and levels of polarization. This paper provides an explanation of state legislative polarization based on socio-economic and demographic factors within the states. Economics and demographics have long played a significant role in understanding party choice, vote choice, the decision to abstain or vote, and support for various policies. Because of this we explore if these factors also influence polarization of state legislatures. Utilizing the Shorr-McCarty polarization data for state legislatures, which provides the differences between the mean Democratic and Republican legislator scores, and controlling for important economic and demographic factors, we explain a significant amount of the polarization existing in state legislatures. These findings present a fascinating look into not only the root causes of polarization in state legislatures, but also point to some fundamental differences in politics and ideology at the state and national levels.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".