When Partisan Identification and Economic Evaluations Conflict: A Closer Look at Conflicted Partisans in the United States
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
Objective Most partisan voters in the United States hold biased perceptions of the state of the national economy. Comparatively little is known, however, about voters who hold economic evaluations that conflict with their partisan identification. Methods I use the American National Election Studies from 1980 to 2016 to conduct over time regression analyses of the identity and behavior of conflicted partisans. Results The share of conflicted partisans is substantial, especially during economic recessions. Conflict is associated with weak levels of party identification, higher levels of nonvoting, and lower levels of in‐party voting. Conclusion A closer look at conflicted partisans suggests that partisan bias in economic judgments fluctuates over time and varies among party affiliates. The study further shows that conflict between party affiliation and economic judgments is associated with differential voting and turnout patterns among party identifiers.
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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 | 0.003 | 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 | 0.001 | 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) | 0.000 | 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