Before the Party Hijacks: The Limited Role of Party Cues in Appraisal of Low-Salience Policies—Experimental Evidence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it