Before the Party Hijacks: The Limited Role of Party Cues in Appraisal of Low-Salience Policies—Experimental Evidence
Bibliographic record
Abstract
Abstract What shapes Americans’ policy preferences: partisanship or policy content? While previous studies have addressed this question, many of them focused on high-salience policies. This raises an identification challenge because the content of such policies contains party cues. The current study employs a diverse set of low-salience policies to discern the unique effects of party cues and policy content, before the issues are “hijacked” by the parties. These policies are embedded in an original conjoint experiment administered among a national US sample. The design enables me to assess the effects of policy content and partisan sponsorship orthogonally. Contrary to previous studies, I find that respondents are attentive to policy content on low-salience issues, and it influences their policy preferences similarly or even more than party cues, across policy domains. Moreover, the support patterns and levels of Democrats and Republicans for many low-salience policies are similar. Party cues, by contrast, polarize partisans’ preferences across domains.
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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 | 0.008 | 0.032 |
| 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 | 0.001 | 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".