It's Not Easy Being Green: Minor Party Labels as Heuristic Aids
Bibliographic record
Abstract
This paper examines if, when, and to what extent U.S. minor party labels influence individual opinions over a range of political issues. Based on data from an experimental study, we reach three general conclusions. First, as cues, party labels are more likely to influence opinions over complex issues. Second, familiarity with and trust in a party condition cue acceptance. Third, as a whole, minor party labels act as effective cues less consistently than major parties. This finding, we suggest, indicates that there exists some threshold level of familiarity and trust that minor parties must reach in the mass public in order to be effective cues. This research is valuable because it extends current work on party labels as heuristic devices and more general work on cues; our findings are additionally important given recent trends in public opinion data, which indicate that the U.S. public is becoming more accepting of minor parties as permanent features of the political system.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".