Learning from Divided Parties? Legislator Dissent as a Cue for Opinion Formation
Bibliographic record
Abstract
Scholars have generally seen united parties as normatively desirable. However, little work has explored the implications of divided parties for public opinion. This article examines whether legislator dissent reduces public support for the policy positions of divided parties. Dissent can do this in two ways: by undermining the consistency of party cues sent to co-partisans of the divided party; or by providing a signal regarding the likely distance of the policy proposal from citizen preferences. These possibilities are evaluated here using a survey experiment. Respondents were exposed to mock news articles about a debate on a bill that manipulated the presence of dissent on government benches and its spatial location—either proximate to the opposition party or located on the government party’s ideological flank. Legislator dissent appears to reduce the support of government policy for opposition co-partisans, but only when it is centrist and for those with high levels of political knowledge. These results suggest legislator dissent can act as a cue, if a complex one, to help citizens form policy evaluations in line with their preferences.
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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 | 0.004 | 0.042 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".